339 Dask Jobs - Page 3

Programming: Python, R, Julia, SQL - Frameworks: TensorFlow, PyTorch, Scikit-Learn, Keras - Tools: Jupyter Notebooks, Vertex AI, AutoML, MLflow - Data Processing: BigQuery, Pandas, NumPy, Dask - Visualization: Matplotlib, Seaborn, Tableau, Power BI - Cloud: GCP AI Platform, Vertex AI, Cloud ML Engine - MLOps: Kubeflow, Vertex Pipelines, Docker, Kubernetes

Programming: Python, R, Julia, SQL - Frameworks: TensorFlow, PyTorch, Scikit-Learn, Keras - Tools: Jupyter Notebooks, Vertex AI, AutoML, MLflow - Data Processing: BigQuery, Pandas, NumPy, Dask - Visualization: Matplotlib, Seaborn, Tableau, Power BI - Cloud: GCP AI Platform, Vertex AI, Cloud ML Engine - MLOps: Kubeflow, Vertex Pipelines, Docker, Kubernetes

Programming: Python, R, Julia, SQL - Frameworks: TensorFlow, PyTorch, Scikit-Learn, Keras - Tools: Jupyter Notebooks, Vertex AI, AutoML, MLflow - Data Processing: BigQuery, Pandas, NumPy, Dask - Visualization: Matplotlib, Seaborn, Tableau, Power BI - Cloud: GCP AI Platform, Vertex AI, Cloud ML Engine - MLOps: Kubeflow, Vertex Pipelines, Docker, Kubernetes

Programming: Python, R, Julia, SQL - Frameworks: TensorFlow, PyTorch, Scikit-Learn, Keras - Tools: Jupyter Notebooks, Vertex AI, AutoML, MLflow - Data Processing: BigQuery, Pandas, NumPy, Dask - Visualization: Matplotlib, Seaborn, Tableau, Power BI - Cloud: GCP AI Platform, Vertex AI, Cloud ML Engine - MLOps: Kubeflow, Vertex Pipelines, Docker, Kubernetes

Programming: Python, R, Julia, SQL - Frameworks: TensorFlow, PyTorch, Scikit-Learn, Keras - Tools: Jupyter Notebooks, Vertex AI, AutoML, MLflow - Data Processing: BigQuery, Pandas, NumPy, Dask - Visualization: Matplotlib, Seaborn, Tableau, Power BI - Cloud: GCP AI Platform, Vertex AI, Cloud ML Engine - MLOps: Kubeflow, Vertex Pipelines, Docker, Kubernetes

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. We are seeking a AI/ML Engineer to lead the design, development, and deployment of agentic AI systems and multi-agent architectures . You will focus on large language models (LLMs), intelligent agents, and Azure-native deployment to automate complex incentive workflows. Collaborating closely with our Data Engineering team (handling pipelines, integrations, and reporting), you will own the AI core functionalities —from document parsing to agent orchestration, scaling solutions iteratively to handle increasing volumes and complexities. Responsibilities Lead the design, development, and deployment of agentic AI and multi-agent systems to automate incentive workflows (ingestion → parsing → calculation → reporting). Apply NLP techniques to parse T&C documents, extracting eligibility rules, identifiers, and business logic into structured outputs (e.g., JSON). Implement prompt engineering, fine-tuning, and inference for LLMs (Azure OpenAI, Hugging Face) to handle business logic, corrections, and decision-making. Architect and modernize cloud-native applications using APIs, microservices, event-driven designs, and containerized deployments (AKS, Functions, Logic Apps). Build and manage MLOps workflows (CI/CD pipelines, retraining, monitoring, governance) to ensure robust AI lifecycle management. Deploy production-grade AI systems at scale with self-correction, monitoring, and failover mechanisms. Implement Retrieval-Augmented Generation (RAG) and knowledge-driven AI pipelines for enhanced reasoning and decision-making. Collaborate with Data Engineers to integrate AI agents with Azure Synapse, SQL, Dataverse, and Power BI for dynamic incentive computation and reporting. Iteratively scale the system: from handling a few T&C use cases to supporting dozens of documents, multiple data sources, and high-volume incentive calculations. Stay up to date on agent orchestration, reinforcement learning, and semantic search to enhance agent autonomy and system efficiency. Skills to have Professional experience in AI/ML engineering, with a strong focus on LLMs and intelligent agent systems. Hands-on expertise with agentic AI frameworks (LangChain, AutoGen, CrewAI, MetaGPT, LangGraph). Proven experience in LLM orchestration and NLP pipelines (Hugging Face, OpenAI APIs, Semantic Kernel, PromptFlow). Strong coding skills in Python (primary), with experience in Java, .NET, TypeScript, C#, or C++. Proficiency in Azure ecosystem: Azure OpenAI, Cognitive Services, Azure ML, Synapse, Data Factory, Microsoft Fabric. Experience in cloud-native deployments (AKS, Functions, Logic Apps, Event Hubs, Key Vault, Application Insights). Knowledge of distributed computing (Ray, Dask), messaging systems (Kafka, Redis), and AI Ops/Security (Gen AI Ops, Sentinel, monitoring). Qualifications Required Qualifications Bachelor’s or master’s degree in computer science, AI, Machine Learning, or a related field. Hands-on experience with Azure OpenAI or similar LLM platforms (e.g., OpenAI API, Hugging Face). Expertise in LLMs, including LangChain for agent building and prompt engineering techniques. Proficiency in Python for developing AI solutions, with strong skills in libraries like LangChain, NLTK, or spaCy for NLP tasks. Proven experience building autonomous AI agents or multi-agent systems for real-world applications, such as business process automation. Familiarity with NLP for parsing unstructured documents (e.g., T&C extraction). Understanding of cloud infrastructure setup, particularly Azure services (e.g., Functions, Key Vault, Application Insights) for deploying and scaling AI workloads. Strong problem-solving skills and ability to work in an iterative, agile environment. Preferred Qualifications Experience scaling AI systems to handle increased workloads, such as more incentive calculations, using serverless architectures or containerization. Knowledge of business domains like incentive management or contract analysis. Familiarity with tools like CrewAI for agent orchestration or Azure ML for model fine-tuning. Experience collaborating with Data Engineers on integrations involving cubes, SQL servers, or Power BI. Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Required Skillset: Minimum of 5 years as a Python Developer, demonstrating expertise through a strong portfolio of diverse projects. Bachelors degree in computer science, Software Engineering, or a closely related field, providing a solid foundation in programming principles. Proficient in Python Ecosystem: - In-depth knowledge of the Python software development stack, frameworks, and tools. - Expertise in libraries such as Numpy, Scipy, Pandas, Dask, spaCy, NLTK, scikit-learn, and PyTorch. - Experience with popular Python web frameworks such as Django, Flask, or Pyramid Database Management: Familiarity with both SQL and NoSQL database technologies for efficient data handling and storage solutions. Exceptional problem-solving abilities to tackle technical challenges effectively. Communication and Teamwork Strong communication skills and the ability to collaborate effectively with cross-functional teams. Proficient in using Docker and Kubernetes for containerization in development and production environments. Preferred Skills & Qualifications: Experience with popular Python web development frameworks such as Django, Flask, or Pyramid. Understanding of data science principles and machine learning concepts, with experience using relevant tools and libraries. Familiarity with Continuous Integration and Continuous Deployment (CI/CD) pipelines and automation tools to streamline development processes. Basic working knowledge of cloud platforms such as AWS, Google Cloud, or Azure for deploying applications and managing services. Contributions to open-source Python projects or active involvement in the Python community, showcasing commitment to continuous learning and collaboration."" "

As a Data Engineer at Zebra, your primary responsibility will be to understand the technical requirements of clients and design and build data pipelines to meet those requirements. In addition to developing solutions, you will also oversee the development of other Engineers. Strong verbal and written communication skills are essential as you will be required to effectively communicate with clients and internal teams. Success in this role will require a deep understanding of databases, SQL, cloud technologies, and modern data integration and orchestration tools like GCP Dataflow, GKE, Workflow, Cloud Build, and Airflow. You will play a critical role in designing and implementing data platforms for AI products, developing productized and parameterized data pipelines, and creating efficient data transformation code in various languages such as Python, Scala, Java, and Dask. You will also be responsible for building workflows to automate data pipelines using Python, Argo, and Cloud Build, developing data validation tests, and conducting performance testing and profiling of the code. In this role, you will guide Data Engineers in delivery teams to follow best practices in deploying data pipeline workflows, build data pipeline frameworks to automate high-volume and real-time data delivery, and operationalize scalable data pipelines to support data science and advanced analytics. You will also optimize customer data science workloads and manage cloud services costs/utilization while developing sustainable data-driven solutions with cutting-edge data technologies. To qualify for this position, you should have a Bachelor's, Master's, or Ph.D. Degree in Computer Science or Engineering, along with at least 5 years of experience programming in languages like Python, Scala, or Go. You should also have extensive experience in SQL, data transformation, developing distributed systems using open-source technologies like Spark and Dask, and working with relational or NoSQL databases. Experience in AWS, Azure, or GCP environments is highly desired, as well as knowledge of data models in the Retail and Consumer products industry and agile project methodologies. Strong communication skills, the ability to learn new technologies quickly and independently, and the capacity to work in a diverse and fast-paced environment are key competencies required for this role. You should be able to work collaboratively in a team setting and achieve stretch goals while teleworking. Travel is not expected for this position to ensure candidate safety and security from online fraudulent activities.,

Required skills and qualifications 5+ years of experience as a Python Developer with a strong portfolio of projects. Bachelor's degree in Computer Science, Software Engineering or a related field. In-depth understanding of the Python software development stacks, ecosystems, frameworks and tools such as Numpy, Scipy, Pandas, Dask, spaCy, NLTK, sci-kit-learn and PyTorch. MATLAB experience is must Experience with front-end development using HTML, CSS, and JavaScript. Familiarity with database technologies such as SQL and NoSQL. Excellent problem-solving ability with solid communication and collaboration skills. Preferred skills and qualifications Experience with popular Python frameworks such as Django, Flask or Pyramid. Knowledge of data science and machine learning concepts and tools. A working understanding of cloud platforms such as AWS, Google Cloud or Azure. Contributions to open-source Python projects or active involvement in the Python community.

Line of Service Advisory Industry/Sector Not Applicable Specialism SAP Management Level Senior Associate Job Description & Summary A career within Enterprise Architecture services, will provide you with the opportunity to bring our clients a competitive advantage through defining their technology objectives, assessing solution options, and devising architectural solutions that help them achieve both strategic goals and meet operational requirements. We help build software and design data platforms, manage large volumes of client data, develop compliance procedures for data management, and continually researching new technologies to drive innovation and sustainable change. *Why PWC At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us . At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations. " Job Description & Summary: We are looking for a seasoned Python Developers Responsibilities: Create large-scale data processing pipelines to help developers build and train novel machine learning algorithms. Participate in code reviews, ensure code quality and identify areas for improvement to implement practical solutions. Debugging codes when required and troubleshooting any Python-related queries. Keep up to date with emerging trends and technologies in Python development. Mandatory skill sets: Strong Python with Experience Data Structure. Strong In OOPS. Problem Solving skills Preferred skill sets: 3+ years of experience as a Python Developer with a strong portfolio of projects. Bachelor's degree in Computer Science, Software Engineering or a related field. In-depth understanding of the Python software development stacks, ecosystems, frameworks and tools such as Numpy, Scipy, Pandas, Dask, spaCy, NLTK, sci-kit-learn and PyTorch. Experience with front-end development using HTML, CSS, and JavaScript. Familiarity with database technologies such as SQL and NoSQL. Excellent problem-solving ability with solid communication and collaboration skills Years of experience required: 4+ + Yrs Education qualification: BE/B.Tech/MBA Education (if blank, degree and/or field of study not specified) Degrees/Field of Study required: Bachelor Degree, Master of Business Administration, Bachelor of Engineering Degrees/Field of Study preferred: Certifications (if blank, certifications not specified) Required Skills Python (Programming Language) Optional Skills Desired Languages (If blank, desired languages not specified) Travel Requirements Available for Work Visa Sponsorship? Government Clearance Required? Job Posting End Date

Senior Full Stack Data Scientist We are seeking a Senior Full Stack Data Scientist who can own the entire lifecycle of data-driven solutions—from data collection and transformation to model deployment and ongoing maintenance. This role requires deep technical expertise, as well as strong communication and leadership skills, to ensure that analytics initiatives align with business goals and consistently deliver measurable impact. About the Team Our data function is a multidisciplinary group of data scientists, engineers, and analysts working together to produce scalable, high-impact data products. We foster a culture of innovation , collaboration , and continuous learning , using state-of-the-art technologies to tackle real business challenges. Key Responsibilities End-to-End Data Product Ownership Design and manage full-stack data solutions from data ingestion (ETL/ELT) to model deployment and performance monitoring. Work with business stakeholders to define project scopes, translate ambiguous requirements into actionable data science tasks, and deliver results. Advanced Analytics s Machine Learning Develop and implement statistical and ML models (e.g., predictive modeling, classification, clustering, time-series forecasting). Employ advanced ML techniques such as Bayesian methods, reinforcement learning, or metaheuristics, as needed. Integrate data science workflows with analytics platforms (e.g., Spark, Dask) for large-scale or real-time processing. Software Engineering s DevOps Follow OOP principles and design patterns in Python for clean, maintainable code. Set up CI/CD pipelines , containerization (Docker), and orchestration (Kubernetes) to enable robust, automated deployments. Optimize performance and manage cost on cloud platforms (Azure, AWS, or GCP) by structuring resources effectively. Front-End s Visualization Build or enhance user-facing dashboards using frameworks like Streamlit , Plotly Dash , or enterprise solutions (e.g., PowerBI). Present actionable insights in a clear, interactive format that resonates with non-technical audiences. Real-Time s Large-Scale Data Architecture Design pipelines for real-time data streaming (e.g., Kafka, Spark Streaming) where business needs require continuous data updates. Work with data engineers to maintain data lakes or data warehouses , ensuring efficient storage and retrieval for diverse use cases. Security s Compliance Adhere to data governance and regulatory guidelines (GDPR, HIPAA, etc.) relevant to your industry. Implement secure coding practices and access controls to protect sensitive data assets. Performance Tuning s Cost Optimization Continuously monitor , profile , and refine data pipelines and ML models to ensure minimal latency and reduced computational costs. Utilize cloud-native monitoring tools (Azure Monitor, AWS CloudWatch, GCP Stackdriver) for alerts , logging , and budget management. Mentorship s Leadership Provide technical guidance and coaching to junior data scientists and data engineers, encouraging best practices. Lead architecture and design reviews, fostering a culture of quality and collaboration across the data organization. What We Look For Education s Experience MS or PhD in Computer Science, Statistics, Mathematics, Artificial Intelligence , or a related field. 8+ years of experience delivering end-to-end data solutions in data science, analytics, or machine learning. Technical Expertise Proficiency in Python , with strong skills in OOP , design patterns , and software engineering best practices. Mastery of ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) and proficiency in NumFOCUS libraries (pandas, NumPy, SciPy). Familiarity with cloud platforms (Azure, AWS, GCP), containerization (Docker), and orchestration (Kubernetes). Experience with version control (Git) and building CI/CD pipelines for data and ML products. Ability to handle large-scale data (Spark, Dask, or similar) and real-time streaming (Kafka, Flink) when required. Analytical s Communication Skills Deep knowledge of statistics , machine learning , and optimization techniques, paired with the ability to convey technical results to diverse audiences. Experience in data visualization and dashboard creation, conveying complex information in an understandable manner. Proven record of collaborating across business, engineering, and product management teams. Soft Skills s Mindset Commitment to continuous learning and staying current with emerging data science and engineering trends. Strong leadership qualities with a knack for mentoring , problem-solving, and managing stakeholder expectations. Flexible and agile approach to adapting in a fast-paced environment and delivering high-quality outputs. Why Join Us Strategic Impact : Work on mission-critical projects where data science informs key decisions and directly influences the bottom line. Cutting-Edge Technology : Leverage a modern tech stack and ample freedom to experiment with new tools and methodologies. Leadership s Growth : Shape the technical direction of the data organization, mentoring talent and establishing best practices. Collaborative Environment : Join a supportive team culture that values shared learning, innovation, and collective problem-solving. Work-Life Balance : Enjoy a flexible schedule with remote-friendly policies and competitive compensation. If you’re passionate about end-to-end data solutions and have the depth of expertise to drive data projects from conception through deployment , we invite you to become our Senior Full Stack Data Scientist . Bring your blend of data science , software engineering , and strategic thinking to propel our organization toward data-driven excellence. Apply now to embark on this exciting journey!

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. We are seeking a AI/ML Engineer to lead the design, development, and deployment of agentic AI systems and multi-agent architectures . You will focus on large language models (LLMs), intelligent agents, and Azure-native deployment to automate complex incentive workflows. Collaborating closely with our Data Engineering team (handling pipelines, integrations, and reporting), you will own the AI core functionalities —from document parsing to agent orchestration, scaling solutions iteratively to handle increasing volumes and complexities. Responsibilities Lead the design, development, and deployment of agentic AI and multi-agent systems to automate incentive workflows (ingestion → parsing → calculation → reporting). Apply NLP techniques to parse T&C documents, extracting eligibility rules, identifiers, and business logic into structured outputs (e.g., JSON). Implement prompt engineering, fine-tuning, and inference for LLMs (Azure OpenAI, Hugging Face) to handle business logic, corrections, and decision-making. Architect and modernize cloud-native applications using APIs, microservices, event-driven designs, and containerized deployments (AKS, Functions, Logic Apps). Build and manage MLOps workflows (CI/CD pipelines, retraining, monitoring, governance) to ensure robust AI lifecycle management. Deploy production-grade AI systems at scale with self-correction, monitoring, and failover mechanisms. Implement Retrieval-Augmented Generation (RAG) and knowledge-driven AI pipelines for enhanced reasoning and decision-making. Collaborate with Data Engineers to integrate AI agents with Azure Synapse, SQL, Dataverse, and Power BI for dynamic incentive computation and reporting. Iteratively scale the system: from handling a few T&C use cases to supporting dozens of documents, multiple data sources, and high-volume incentive calculations. Stay up to date on agent orchestration, reinforcement learning, and semantic search to enhance agent autonomy and system efficiency. Skills to have Professional experience in AI/ML engineering, with a strong focus on LLMs and intelligent agent systems. Hands-on expertise with agentic AI frameworks (LangChain, AutoGen, CrewAI, MetaGPT, LangGraph). Proven experience in LLM orchestration and NLP pipelines (Hugging Face, OpenAI APIs, Semantic Kernel, PromptFlow). Strong coding skills in Python (primary), with experience in Java, .NET, TypeScript, C#, or C++. Proficiency in Azure ecosystem: Azure OpenAI, Cognitive Services, Azure ML, Synapse, Data Factory, Microsoft Fabric. Experience in cloud-native deployments (AKS, Functions, Logic Apps, Event Hubs, Key Vault, Application Insights). Knowledge of distributed computing (Ray, Dask), messaging systems (Kafka, Redis), and AI Ops/Security (Gen AI Ops, Sentinel, monitoring). Qualifications Required Qualifications Bachelor’s or master’s degree in computer science, AI, Machine Learning, or a related field. Hands-on experience with Azure OpenAI or similar LLM platforms (e.g., OpenAI API, Hugging Face). Expertise in LLMs, including LangChain for agent building and prompt engineering techniques. Proficiency in Python for developing AI solutions, with strong skills in libraries like LangChain, NLTK, or spaCy for NLP tasks. Proven experience building autonomous AI agents or multi-agent systems for real-world applications, such as business process automation. Familiarity with NLP for parsing unstructured documents (e.g., T&C extraction). Understanding of cloud infrastructure setup, particularly Azure services (e.g., Functions, Key Vault, Application Insights) for deploying and scaling AI workloads. Strong problem-solving skills and ability to work in an iterative, agile environment. Preferred Qualifications Experience scaling AI systems to handle increased workloads, such as more incentive calculations, using serverless architectures or containerization. Knowledge of business domains like incentive management or contract analysis. Familiarity with tools like CrewAI for agent orchestration or Azure ML for model fine-tuning. Experience collaborating with Data Engineers on integrations involving cubes, SQL servers, or Power BI. Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Location: Remote Filling the google form mentioned below is mandatory. Please go to how to apply section and fill up the form. About Purple Merit At Purple Merit, we are pioneering the future of autonomous multi-agent AI systems . Our mission is to empower enterprises with intelligent, self-directed, and context-driven AI agents that can reason, plan, and execute complex tasks with precision and reliability. We foster a culture of innovation, continuous learning, and ownership —giving you the freedom and support to push boundaries and build transformative AI technologies. Here, your work powers real change fueling business process transformation on a global scale—and you’ll have unmatched freedom to innovate, propose research, and see your ideas come to life in production environments. We’re passionate about nurturing AI leaders who thrive on solving real-world enterprise challenges using generative AI and multi-agent frameworks . Role Overview As an AI/ML Engineer intern at Purple Merit, you will: You’ll architect intelligent agents with reasoning capabilities, develop RAG pipelines and context engineering solutions, and implement Model Context Protocol (MCP) servers for enterprise data connectivity. Work with production-grade technologies including agent-to-agent communication protocols (A2A, JSON-RPC), deploy scalable AI applications on AWS/Kubernetes, and collaborate cross-functionally on real-world agentic AI projects that shape the future of autonomous intelligence Work closely with cross-functional teams to drive integration, automation, and innovation. Key Responsibilities Design and implement intelligent, autonomous agents with goal-driven action and dynamic task assignment . Build multi-agent workflows integrating perception, reasoning, and action across AI tools, APIs, and knowledge bases. Implement RAG pipelines and agent memory systems grounding agents in domain-specific knowledge. Develop workflows with LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel , and orchestration tools like n8n . Build, deploy, and manage MCP servers/clients with JSON-RPC 2.0 , transport layers ( stdio, HTTP, WebSocket ), and custom extensions. Implement real-time agent communication via SSE , WebSockets , and structured A2A protocols . Fine-tune large and small language models ( LLMs, SLMs ) with LoRA, custom tokenizers, quantization techniques. Integrate generative AI frameworks such as Google ADK, LangGraph, AutoGen, CrewAI, OpenAI Agents SDK, DSPy, LiteLLM, Langfuse, MemGPT, mem0 . Build scalable AI pipelines using AWS Lambda, IAM, CloudFormation, and Infrastructure as Code (CDK) . Write clean, testable, and scalable Python code following best practices. Use MLflow and Weights & Biases for experiment tracking, versioning, and deployment. Work in security-compliant, large-scale enterprise environments ensuring reliability . Design intuitive UIs and robust APIs enabling human-in-the-loop feedback and collaborative workflows. Integrate external data sources, APIs, and databases into AI agent workflows for dynamic decision-making. Participate in AI safety, alignment , adversarial testing, and implement guardrails to ensure secure, ethical agent behavior . Implement scalable distributed agent orchestration, fault tolerance, load balancing , and self-healing workflows . Develop explainable AI pipelines and advanced monitoring for observability and auditing. Work with SQL/NoSQL ( PostgreSQL, MongoDB ), vector DBs ( FAISS, Pinecone ), and data analysis libraries ( NumPy, Pandas, SciPy, Seaborn, Plotly ). Required Skills & Qualifications Strong Python programming skills with debugging, testing, and scalable code development. Foundational knowledge of designing and building MCP servers, clients , and transport layers ( stdio, HTTP, WebSocket ) with JSON-RPC 2.0 protocol . Familiarity with agent communication protocols ( A2A, JSON-RPC 2.0, WebSockets ). Experience or strong interest in building multi-agent AI systems and generative AI . Ability to write clean, maintainable code and collaborate effectively in remote teams . Passion for learning and eagerness to contribute to designing secure MCP endpoints and extending protocol specifications. Implement and maintain robust Continuous Integration and Continuous Deployment (CI/CD) pipelines to ensure seamless automated testing, deployment, and monitoring of AI/ML models and autonomous agent systems. Good remote communication skills and discipline to work in a fixed schedule environment. Mandatory Experience with generative AI frameworks and libraries such as LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, DSPy, MemGPT, LiteLLM, Langfuse, Mem0 . Knowledge of reinforcement, supervised, and unsupervised learning to enhance agent capabilities. Familiarity with cloud platforms ( AWS/GCP/Azure ) and MLOps tools like MLflow, Weights & Biases . Experience with containerization ( Docker ), orchestration ( Kubernetes ), and distributed ML ( Spark MLlib, Dask-ML, Ray, Horovod ). Exposure to AI safety, ethics , and adversarial testing methodologies. Internship & Salary Structure Duration: 5 months internship (remote) Stipend: ₹12,000 per month during internship Conversion: Upon successful internship completion, full-time salary range of ₹3 LPA to ₹9 LPA will be offered based on performance, leadership, and technical skills demonstrated. Note: There is no direct full-time hiring ; only internship-to-full-time conversion is available. Things to Keep in Mind No laptop or physical assets will be provided by PurpleMerit; candidates must use their own systems. Fully remote role requiring your own laptop/PC ( preferably 16GB or 32GB RAM ) to handle development tasks efficiently. Fixed work hours: Monday to Saturday, 9:30 AM – 6:30 PM IST . Freelancing or other external paid work during the internship is prohibited and may lead to termination. Strong remote communication skills and experience working in distributed teams are essential. Completion of the full 5-month internship is mandatory for consideration for full-time roles. Why Join Purple Merit? At Purple Merit, you’re not just joining a company you’re joining a movement pioneering agentic AI that transforms industries. We offer: The freedom to innovate and lead real-world projects that challenge the status quo. A culture rooted in continuous learning, experimentation, and collaboration . A chance to work with cutting-edge AI technologies shaping the future of autonomous systems. Opportunities to grow into leadership roles and publish research. Remote flexibility allowing you to work from anywhere in India while being part of a dynamic, dedicated team. How to Apply Only candidates willing to commit fully to the internship duration and motivated by the role should apply. To be considered, please: Fill out the application form  https://forms.gle/muWe2gvUF1UFiSmi7 The link is cannot be accessed from here. Please copy and paste the link in your browser and fill the form. Filling the google form is mandatory. Only those applications will be considered. Submit your updated resume and any relevant portfolio or GitHub links on the application form only. If any technical issues persist, write to the email ID mentioned in Form description. Incomplete applications or direct contacts without form submission will not be considered. Final Note We’re seeking a true architect of agentic AI not generalists or casual learners, but passionate builders who have hands-on experience or a strong desire to build systems like AutoGPT, LangGraph, or ReAct-based agents . If you want to lead and innovate in autonomous AI at scale, Purple Merit is the next frontier for you.

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. We are seeking a AI/ML Engineer to lead the design, development, and deployment of agentic AI systems and multi-agent architectures . You will focus on large language models (LLMs), intelligent agents, and Azure-native deployment to automate complex incentive workflows. Collaborating closely with our Data Engineering team (handling pipelines, integrations, and reporting), you will own the AI core functionalities —from document parsing to agent orchestration, scaling solutions iteratively to handle increasing volumes and complexities. Responsibilities Lead the design, development, and deployment of agentic AI and multi-agent systems to automate incentive workflows (ingestion → parsing → calculation → reporting). Apply NLP techniques to parse T&C documents, extracting eligibility rules, identifiers, and business logic into structured outputs (e.g., JSON). Implement prompt engineering, fine-tuning, and inference for LLMs (Azure OpenAI, Hugging Face) to handle business logic, corrections, and decision-making. Architect and modernize cloud-native applications using APIs, microservices, event-driven designs, and containerized deployments (AKS, Functions, Logic Apps). Build and manage MLOps workflows (CI/CD pipelines, retraining, monitoring, governance) to ensure robust AI lifecycle management. Deploy production-grade AI systems at scale with self-correction, monitoring, and failover mechanisms. Implement Retrieval-Augmented Generation (RAG) and knowledge-driven AI pipelines for enhanced reasoning and decision-making. Collaborate with Data Engineers to integrate AI agents with Azure Synapse, SQL, Dataverse, and Power BI for dynamic incentive computation and reporting. Iteratively scale the system: from handling a few T&C use cases to supporting dozens of documents, multiple data sources, and high-volume incentive calculations. Stay up to date on agent orchestration, reinforcement learning, and semantic search to enhance agent autonomy and system efficiency. Skills to have Professional experience in AI/ML engineering, with a strong focus on LLMs and intelligent agent systems. Hands-on expertise with agentic AI frameworks (LangChain, AutoGen, CrewAI, MetaGPT, LangGraph). Proven experience in LLM orchestration and NLP pipelines (Hugging Face, OpenAI APIs, Semantic Kernel, PromptFlow). Strong coding skills in Python (primary), with experience in Java, .NET, TypeScript, C#, or C++. Proficiency in Azure ecosystem: Azure OpenAI, Cognitive Services, Azure ML, Synapse, Data Factory, Microsoft Fabric. Experience in cloud-native deployments (AKS, Functions, Logic Apps, Event Hubs, Key Vault, Application Insights). Knowledge of distributed computing (Ray, Dask), messaging systems (Kafka, Redis), and AI Ops/Security (Gen AI Ops, Sentinel, monitoring). Qualifications Required Qualifications Bachelor’s or master’s degree in computer science, AI, Machine Learning, or a related field. Hands-on experience with Azure OpenAI or similar LLM platforms (e.g., OpenAI API, Hugging Face). Expertise in LLMs, including LangChain for agent building and prompt engineering techniques. Proficiency in Python for developing AI solutions, with strong skills in libraries like LangChain, NLTK, or spaCy for NLP tasks. Proven experience building autonomous AI agents or multi-agent systems for real-world applications, such as business process automation. Familiarity with NLP for parsing unstructured documents (e.g., T&C extraction). Understanding of cloud infrastructure setup, particularly Azure services (e.g., Functions, Key Vault, Application Insights) for deploying and scaling AI workloads. Strong problem-solving skills and ability to work in an iterative, agile environment. Preferred Qualifications Experience scaling AI systems to handle increased workloads, such as more incentive calculations, using serverless architectures or containerization. Knowledge of business domains like incentive management or contract analysis. Familiarity with tools like CrewAI for agent orchestration or Azure ML for model fine-tuning. Experience collaborating with Data Engineers on integrations involving cubes, SQL servers, or Power BI. Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

We are seeking a Senior Full Stack Data Scientist who can own the entire lifecycle of data-driven solutions from data collection and transformation to model deployment and ongoing maintenance. This role requires deep technical expertise, as well as strong communication and leadership skills, to ensure that analytics initiatives align with business goals and consistently deliver measurable impact. About the Team Our data function is a multidisciplinary group of data scientists, engineers, and analysts working together to produce scalable, high-impact data products. We foster a culture of innovation, collaboration, and continuous learning, using state-of-the-art technologies to tackle real business challenges. _________________________________________________________________________________________________________________ Key Responsibilities 1. End-to-End Data Product Ownership o Design and manage full-stack data solutions from data ingestion (ETL/ELT) to model deployment and performance monitoring. o Work with business stakeholders to define project scopes, translate ambiguous requirements into actionable data science tasks, and deliver results. 2. Advanced Analytics & Machine Learning o Develop and implement statistical and ML models (e.g., predictive modeling, classification, clustering, time-series forecasting). o Employ advanced ML techniques such as Bayesian methods, reinforcement learning, or metaheuristics, as needed. o Integrate data science workflows with analytics platforms (e.g., Spark, Dask) for large-scale or real-time processing. 3. Software Engineering & DevOps o Follow OOP principles and design patterns in Python for clean, maintainable code. o Set up CI/CD pipelines, containerization (Docker), and orchestration (Kubernetes) to enable robust, automated deployments. o Optimize performance and manage cost on cloud platforms (Azure, AWS, or GCP) by structuring resources effectively. 4. Front-End & Visualization o Build or enhance user-facing dashboards using frameworks like Streamlit, Plotly Dash, or enterprise solutions (e.g., PowerBI). o Present actionable insights in a clear, interactive format that resonates with non-technical audiences. 5. Real-Time & Large-Scale Data Architecture o Design pipelines for real-time data streaming (e.g., Kafka, Spark Streaming) where business needs require continuous data updates. o Work with data engineers to maintain data lakes or data warehouses, ensuring efficient storage and retrieval for diverse use cases. 6. Security & Compliance o Adhere to data governance and regulatory guidelines (GDPR, HIPAA, etc.) relevant to your industry. o Implement secure coding practices and access controls to protect sensitive data assets. 7. Performance Tuning & Cost Optimization o Continuously monitor, profile, and refine data pipelines and ML models to ensure minimal latency and reduced computational costs. o Utilize cloud-native monitoring tools (Azure Monitor, AWS CloudWatch, GCP Stackdriver) for alerts, logging, and budget management. 8. Mentorship & Leadership o Provide technical guidance and coaching to junior data scientists and data engineers, encouraging best practices. o Lead architecture and design reviews, fostering a culture of quality and collaboration across the data organization. _________________________________________________________________________________________________________________ What We Look For • Education & Experience o MS or PhD in Computer Science, Statistics, Mathematics, Artificial Intelligence, or a related field. o 8+ years of experience delivering end-to-end data solutions in data science, analytics, or machine learning • Technical Expertise o Proficiency in Python, with strong skills in OOP, design patterns, and software engineering best practices. o Mastery of ML frameworks (e.g., scikit-learn, TensorFlow, PyTorch) and proficiency in NumFOCUS libraries (pandas, NumPy, SciPy). o Familiarity with cloud platforms (Azure, AWS, GCP), containerization (Docker), and orchestration (Kubernetes). o Experience with version control (Git) and building CI/CD pipelines for data and ML products. o Ability to handle large-scale data (Spark, Dask, or similar) and real-time streaming (Kafka, Flink) when required • Analytical & Communication Skills o Deep knowledge of statistics, machine learning, and optimization techniques, paired with the ability to convey technical results to diverse audiences. o Experience in data visualization and dashboard creation, conveying complex information in an understandable manner. o Proven record of collaborating across business, engineering, and product management teams. • Soft Skills & Mindset o Commitment to continuous learning and staying current with emerging data science and engineering trends. o Strong leadership qualities with a knack for mentoring, problem-solving, and managing stakeholder expectations. o Flexible and agile approach to adapting in a fast-paced environment and delivering high-quality outputs. _________________________________________________________________________________________________________________ Why Join Us • Strategic Impact: Work on mission-critical projects where data science informs key decisions and directly influences the bottom line. • Cutting-Edge Technology: Leverage a modern tech stack and ample freedom to experiment with new tools and methodologies. • Leadership & Growth: Shape the technical direction of the data organization, mentoring talent and establishing best practices. • Collaborative Environment: Join a supportive team culture that values shared learning, innovation, and collective problem-solving. • Work-Life Balance: Enjoy a flexible schedule with remote-friendly policies and competitive compensation. _________________________________________________________________________________________________________________ If you’re passionate about end-to-end data solutions and have the depth of expertise to drive data projects from conception through deployment, we invite you to become our Senior Full Stack Data Scientist. Bring your blend of data science, software engineering, and strategic thinking to propel our organization toward data-driven excellence. Apply now to embark on this exciting journey

Microsoft’s mission is to empower every person and every organization on the planet to achieve more. As employees we come together with a growth mindset, innovate to empower others, and collaborate to realize our shared goals. Each day we build on our values of respect, integrity, and accountability to create a culture of inclusion where everyone can thrive at work and beyond. We are seeking a AI/ML Engineer to lead the design, development, and deployment of agentic AI systems and multi-agent architectures . You will focus on large language models (LLMs), intelligent agents, and Azure-native deployment to automate complex incentive workflows. Collaborating closely with our Data Engineering team (handling pipelines, integrations, and reporting), you will own the AI core functionalities —from document parsing to agent orchestration, scaling solutions iteratively to handle increasing volumes and complexities. Responsibilities Lead the design, development, and deployment of agentic AI and multi-agent systems to automate incentive workflows (ingestion → parsing → calculation → reporting). Apply NLP techniques to parse T&C documents, extracting eligibility rules, identifiers, and business logic into structured outputs (e.g., JSON). Implement prompt engineering, fine-tuning, and inference for LLMs (Azure OpenAI, Hugging Face) to handle business logic, corrections, and decision-making. Architect and modernize cloud-native applications using APIs, microservices, event-driven designs, and containerized deployments (AKS, Functions, Logic Apps). Build and manage MLOps workflows (CI/CD pipelines, retraining, monitoring, governance) to ensure robust AI lifecycle management. Deploy production-grade AI systems at scale with self-correction, monitoring, and failover mechanisms. Implement Retrieval-Augmented Generation (RAG) and knowledge-driven AI pipelines for enhanced reasoning and decision-making. Collaborate with Data Engineers to integrate AI agents with Azure Synapse, SQL, Dataverse, and Power BI for dynamic incentive computation and reporting. Iteratively scale the system: from handling a few T&C use cases to supporting dozens of documents, multiple data sources, and high-volume incentive calculations. Stay up to date on agent orchestration, reinforcement learning, and semantic search to enhance agent autonomy and system efficiency. Skills to have Professional experience in AI/ML engineering, with a strong focus on LLMs and intelligent agent systems. Hands-on expertise with agentic AI frameworks (LangChain, AutoGen, CrewAI, MetaGPT, LangGraph). Proven experience in LLM orchestration and NLP pipelines (Hugging Face, OpenAI APIs, Semantic Kernel, PromptFlow). Strong coding skills in Python (primary), with experience in Java, .NET, TypeScript, C#, or C++. Proficiency in Azure ecosystem: Azure OpenAI, Cognitive Services, Azure ML, Synapse, Data Factory, Microsoft Fabric. Experience in cloud-native deployments (AKS, Functions, Logic Apps, Event Hubs, Key Vault, Application Insights). Knowledge of distributed computing (Ray, Dask), messaging systems (Kafka, Redis), and AI Ops/Security (Gen AI Ops, Sentinel, monitoring). Qualifications Required Qualifications Bachelor’s or master’s degree in computer science, AI, Machine Learning, or a related field. Hands-on experience with Azure OpenAI or similar LLM platforms (e.g., OpenAI API, Hugging Face). Expertise in LLMs, including LangChain for agent building and prompt engineering techniques. Proficiency in Python for developing AI solutions, with strong skills in libraries like LangChain, NLTK, or spaCy for NLP tasks. Proven experience building autonomous AI agents or multi-agent systems for real-world applications, such as business process automation. Familiarity with NLP for parsing unstructured documents (e.g., T&C extraction). Understanding of cloud infrastructure setup, particularly Azure services (e.g., Functions, Key Vault, Application Insights) for deploying and scaling AI workloads. Strong problem-solving skills and ability to work in an iterative, agile environment. Preferred Qualifications Experience scaling AI systems to handle increased workloads, such as more incentive calculations, using serverless architectures or containerization. Knowledge of business domains like incentive management or contract analysis. Familiarity with tools like CrewAI for agent orchestration or Azure ML for model fine-tuning. Experience collaborating with Data Engineers on integrations involving cubes, SQL servers, or Power BI. Microsoft is an equal opportunity employer. Consistent with applicable law, all qualified applicants will receive consideration for employment without regard to age, ancestry, citizenship, color, family or medical care leave, gender identity or expression, genetic information, immigration status, marital status, medical condition, national origin, physical or mental disability, political affiliation, protected veteran or military status, race, ethnicity, religion, sex (including pregnancy), sexual orientation, or any other characteristic protected by applicable local laws, regulations and ordinances. If you need assistance and/or a reasonable accommodation due to a disability during the application process, read more about requesting accommodations.

Category Engineering Experience Manager Primary Address Bangalore, Karnataka Overview Voyager (94001), India, Bangalore, Karnataka Lead Machine Learning Engineer Lead Machine Learning Engineer (Gen AI) Generative AI Observability & Governance for ML Platform At Capital One India, we work in a fast paced and intellectually rigorous environment to solve fundamental business problems at scale. Using advanced analytics, data science and machine learning, we derive valuable insights about product and process design, consumer behavior, regulatory and credit risk, and more from large volumes of data, and use it to build cutting edge patentable products that drive the business forward. We’re looking for a Lead ML Engineer to join the Machine Learning Experience (MLX) team ! As a Capital One Lead Engineer, MLE , you'll be part of a team focusing on observability and model governance automation for cutting edge generative AI use cases. You will work on building solutions to collect metadata, metrics and insights from the large scale genAI platform. And build intelligent and smart solutions to derive deep insights into platform's use-cases performance and compliance with industry standards. You will contribute to building a system to do this for Capital One models, accelerating the move from fully trained models to deployable model artifacts ready to be used to fuel business decisioning and build an observability platform to monitor the models and platform components. The MLX team is at the forefront of how Capital One builds and deploys well-managed ML models and features. We onboard and educate associates on the ML platforms and products that the whole company uses. We drive new innovation and research and we’re working to seamlessly infuse ML into the fabric of the company. The ML experience we're creating today is the foundation that enables each of our businesses to deliver next-generation ML-driven products and services for our customers. What You’ll Do: Lead the design and implementation of observability tools and dashboards that provide actionable insights into platform performance and health. Leverage Generative AI models and fine tune them to enhance observability capabilities, such as anomaly detection, predictive analytics, and troubleshooting copilot. Build and deploy well-managed core APIs and SDKs for observability of LLMs and proprietary Gen-AI Foundation Models including training, pre-training, fine-tuning and prompting. Stay abreast of the latest trends in Generative AI, platform observability, responsible AI, and drive the adoption of emerging technologies and methodologies. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state of the art, next generation gen-ai applications. Bring research mindset, lead Proof of concept to showcase capabilities of large language models in the realm of observability and governance which enables practical production solutions for improving platform users productivity. Basic Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. Atleast 7 years of experience in machine learning engineering, building data intensive solutions using distributed computing. Hands-on experience with Generative AI models and their application in observability or related areas. At least 8 years of experience programming with Python, Go, or Java At least 5 years of experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow. At least 5 years of experience productionizing, monitoring, and maintaining models. Experience with cloud platforms like AWS, Azure, or GCP. Atleast 7 years of experience in developing performant, resilient, and maintainable code. Preferred Qualifications: Master's or doctoral degree in data science/computer science, electrical engineering, mathematics, or a similar field. Experience in machine learning, particularly in deploying and operationalizing ML models. Familiarity with container orchestration tools like Kubernetes and Docker. Knowledge of data governance and compliance, particularly in the context of machine learning and AI systems. Prior experience in NVIDIA GPU Telemetry and experience in CUDA Contributed to open source ML software. Authored/co-authored papers, patent on ML techniques, model, or proof of concept. 2+ Experience in developing applications using Generative AI i.e open source or commercial LLMs. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC). This carousel contains a column of headings. Selecting a heading will change the main content in the carousel that follows. Use the Previous and Next buttons to cycle through all the options, use Enter to select. This carousel shows one item at a time. Use the preceding navigation carousel to select a specific heading to display the content here. How We Hire We take finding great coworkers pretty seriously. Step 1 Apply It only takes a few minutes to complete our application and assessment. Step 2 Screen and Schedule If your application is a good match you’ll hear from one of our recruiters to set up a screening interview. Step 3 Interview(s) Now’s your chance to learn about the job, show us who you are, share why you would be a great addition to the team and determine if Capital One is the place for you. Step 4 Decision The team will discuss — if it’s a good fit for us and you, we’ll make it official! How to Pick the Perfect Career Opportunity Overwhelmed by a tough career choice? Read these tips from Devon Rollins, Senior Director of Cyber Intelligence, to help you accept the right offer with confidence. Your wellbeing is our priority Our benefits and total compensation package is designed for the whole person. Caring for both you and your family. Healthy Body, Healthy Mind You have options and we have the tools to help you decide which health plans best fit your needs. Save Money, Make Money Secure your present, plan for your future and reduce expenses along the way. Time, Family and Advice Options for your time, opportunities for your family, and advice along the way. It’s time to BeWell. Career Journey Here’s how the team fits together. We’re big on growth and knowing who and how coworkers can best support you.

Category Engineering Experience Manager Primary Address Bangalore, Karnataka Overview Voyager (94001), India, Bangalore, Karnataka Lead Machine Learning Engineer Lead Machine Learning Engineer (Gen AI) Generative AI Observability & Governance for ML Platform At Capital One India, we work in a fast paced and intellectually rigorous environment to solve fundamental business problems at scale. Using advanced analytics, data science and machine learning, we derive valuable insights about product and process design, consumer behavior, regulatory and credit risk, and more from large volumes of data, and use it to build cutting edge patentable products that drive the business forward. We’re looking for a Lead ML Engineer to join the Machine Learning Experience (MLX) team ! As a Capital One Lead Engineer, MLE , you'll be part of a team focusing on observability and model governance automation for cutting edge generative AI use cases. You will work on building solutions to collect metadata, metrics and insights from the large scale genAI platform. And build intelligent and smart solutions to derive deep insights into platform's use-cases performance and compliance with industry standards. You will contribute to building a system to do this for Capital One models, accelerating the move from fully trained models to deployable model artifacts ready to be used to fuel business decisioning and build an observability platform to monitor the models and platform components. The MLX team is at the forefront of how Capital One builds and deploys well-managed ML models and features. We onboard and educate associates on the ML platforms and products that the whole company uses. We drive new innovation and research and we’re working to seamlessly infuse ML into the fabric of the company. The ML experience we're creating today is the foundation that enables each of our businesses to deliver next-generation ML-driven products and services for our customers. What You’ll Do: Lead the design and implementation of observability tools and dashboards that provide actionable insights into platform performance and health. Leverage Generative AI models and fine tune them to enhance observability capabilities, such as anomaly detection, predictive analytics, and troubleshooting copilot. Build and deploy well-managed core APIs and SDKs for observability of LLMs and proprietary Gen-AI Foundation Models including training, pre-training, fine-tuning and prompting. Stay abreast of the latest trends in Generative AI, platform observability, responsible AI, and drive the adoption of emerging technologies and methodologies. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state of the art, next generation gen-ai applications. Bring research mindset, lead Proof of concept to showcase capabilities of large language models in the realm of observability and governance which enables practical production solutions for improving platform users productivity. Basic Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. Atleast 7 years of experience in machine learning engineering, building data intensive solutions using distributed computing. Hands-on experience with Generative AI models and their application in observability or related areas. At least 8 years of experience programming with Python, Go, or Java At least 5 years of experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow. At least 5 years of experience productionizing, monitoring, and maintaining models. Experience with cloud platforms like AWS, Azure, or GCP. Atleast 7 years of experience in developing performant, resilient, and maintainable code. Preferred Qualifications: Master's or doctoral degree in data science/computer science, electrical engineering, mathematics, or a similar field. Experience in machine learning, particularly in deploying and operationalizing ML models. Familiarity with container orchestration tools like Kubernetes and Docker. Knowledge of data governance and compliance, particularly in the context of machine learning and AI systems. Prior experience in NVIDIA GPU Telemetry and experience in CUDA Contributed to open source ML software. Authored/co-authored papers, patent on ML techniques, model, or proof of concept. 2+ Experience in developing applications using Generative AI i.e open source or commercial LLMs. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Voyager (94001), India, Bangalore, Karnataka Lead Machine Learning Engineer Lead Machine Learning Engineer (Gen AI) Generative AI Observability & Governance for ML Platform At Capital One India, we work in a fast paced and intellectually rigorous environment to solve fundamental business problems at scale. Using advanced analytics, data science and machine learning, we derive valuable insights about product and process design, consumer behavior, regulatory and credit risk, and more from large volumes of data, and use it to build cutting edge patentable products that drive the business forward. We’re looking for a Lead ML Engineer to join the Machine Learning Experience (MLX) team ! As a Capital One Lead Engineer, MLE , you'll be part of a team focusing on observability and model governance automation for cutting edge generative AI use cases. You will work on building solutions to collect metadata, metrics and insights from the large scale genAI platform. And build intelligent and smart solutions to derive deep insights into platform's use-cases performance and compliance with industry standards. You will contribute to building a system to do this for Capital One models, accelerating the move from fully trained models to deployable model artifacts ready to be used to fuel business decisioning and build an observability platform to monitor the models and platform components. The MLX team is at the forefront of how Capital One builds and deploys well-managed ML models and features. We onboard and educate associates on the ML platforms and products that the whole company uses. We drive new innovation and research and we’re working to seamlessly infuse ML into the fabric of the company. The ML experience we're creating today is the foundation that enables each of our businesses to deliver next-generation ML-driven products and services for our customers. What You’ll Do: Lead the design and implementation of observability tools and dashboards that provide actionable insights into platform performance and health. Leverage Generative AI models and fine tune them to enhance observability capabilities, such as anomaly detection, predictive analytics, and troubleshooting copilot. Build and deploy well-managed core APIs and SDKs for observability of LLMs and proprietary Gen-AI Foundation Models including training, pre-training, fine-tuning and prompting. Stay abreast of the latest trends in Generative AI, platform observability, responsible AI, and drive the adoption of emerging technologies and methodologies. Collaborate as part of a cross-functional Agile team to create and enhance software that enables state of the art, next generation gen-ai applications. Bring research mindset, lead Proof of concept to showcase capabilities of large language models in the realm of observability and governance which enables practical production solutions for improving platform users productivity. Basic Qualifications: Bachelor’s or Master’s degree in Computer Science, Engineering, or related field. Atleast 7 years of experience in machine learning engineering, building data intensive solutions using distributed computing. Hands-on experience with Generative AI models and their application in observability or related areas. At least 8 years of experience programming with Python, Go, or Java At least 5 years of experience with an industry recognized ML framework such as scikit-learn, PyTorch, Dask, Spark, or TensorFlow. At least 5 years of experience productionizing, monitoring, and maintaining models. Experience with cloud platforms like AWS, Azure, or GCP. Atleast 7 years of experience in developing performant, resilient, and maintainable code. Preferred Qualifications: Master's or doctoral degree in data science/computer science, electrical engineering, mathematics, or a similar field. Experience in machine learning, particularly in deploying and operationalizing ML models. Familiarity with container orchestration tools like Kubernetes and Docker. Knowledge of data governance and compliance, particularly in the context of machine learning and AI systems. Prior experience in NVIDIA GPU Telemetry and experience in CUDA Contributed to open source ML software. Authored/co-authored papers, patent on ML techniques, model, or proof of concept. 2+ Experience in developing applications using Generative AI i.e open source or commercial LLMs. No agencies please. Capital One is an equal opportunity employer (EOE, including disability/vet) committed to non-discrimination in compliance with applicable federal, state, and local laws. Capital One promotes a drug-free workplace. Capital One will consider for employment qualified applicants with a criminal history in a manner consistent with the requirements of applicable laws regarding criminal background inquiries, including, to the extent applicable, Article 23-A of the New York Correction Law; San Francisco, California Police Code Article 49, Sections 4901-4920; New York City’s Fair Chance Act; Philadelphia’s Fair Criminal Records Screening Act; and other applicable federal, state, and local laws and regulations regarding criminal background inquiries. If you have visited our website in search of information on employment opportunities or to apply for a position, and you require an accommodation, please contact Capital One Recruiting at 1-800-304-9102 or via email at RecruitingAccommodation@capitalone.com. All information you provide will be kept confidential and will be used only to the extent required to provide needed reasonable accommodations. For technical support or questions about Capital One's recruiting process, please send an email to Careers@capitalone.com Capital One does not provide, endorse nor guarantee and is not liable for third-party products, services, educational tools or other information available through this site. Capital One Financial is made up of several different entities. Please note that any position posted in Canada is for Capital One Canada, any position posted in the United Kingdom is for Capital One Europe and any position posted in the Philippines is for Capital One Philippines Service Corp. (COPSSC).

Key Responsibilities. Predictive Modeling & Deep Learning:. Develop ML/DL models for predicting match scores & outcomes, team & player performances, and statistics. Implement time-series forecasting models (LSTMs, Transformers, ARIMA, etc.) for score predictions. Train and fine-tune reinforcement learning models for strategic cricket decision-making. Develop ensemble learning techniques to improve predictive accuracy. In-Depth Cricket Analytics:. Design models to analyze player form, team strengths, matchups, and opposition weaknesses. Build player impact and performance forecasting models based on pitch conditions, opposition, and recent form. Extract insights from match footage and live tracking data using deep learning-based video analytics. Data Processing & Engineering:. Collect, clean, and preprocess structured and unstructured cricket datasets from APIs, scorecards, and video feeds. Build data pipelines (ETL) for real-time and historical data ingestion. Work with large-scale datasets using big data tools (Spark, Hadoop, Dask, etc.). Model Deployment & MLOps:. Deploy ML/DL models into production environments (AWS, GCP, Azure etc). Develop APIs to serve predictive models for real-time applications. Implement CI/CD pipelines, model monitoring, and retraining workflows for continuous improvement. Performance Metrics & Model Explainability:. Define and optimize evaluation metrics (MAE, RMSE, ROC-AUC, etc.) for model performance tracking. Implement explainable AI techniques to improve model transparency. Continuously update models with new match data, player form & injuries, and team form changes. About CompanyAt Lifease Solutions LLP, we believe that design and technology are the perfect blend to solve any problem and bring any idea to life. Lifease Solutions is a leading provider of software solutions and services that help businesses succeed. Based in Noida, India, we are committed to delivering high-quality, innovative solutions that drive value and growth for our customers. Our expertise in the finance, sports, and capital market domains has made us a trusted partner for companies around the globe. We take pride in our ability to turn small projects into big successes, and we are always looking for ways to help our clients maximize their IT investments

Join us at Provectus to be a part of a team that is dedicated to building cutting-edge technology solutions that have a positive impact on society. Our company specializes in AI and ML technologies, cloud services, and data engineering, and we take pride in our ability to innovate and push the boundaries of what's possible. As an ML Engineer, you’ll be provided with all opportunities for development and growth. Let's work together to build a better future for everyone! Requirements: Comfortable with standard ML algorithms and underlying math. Strong hands-on experience with LLMs in production, RAG architecture, and agentic systems AWS Bedrock experience strongly preferred Practical experience with solving classification and regression tasks in general, feature engineering. Practical experience with ML models in production. Practical experience with one or more use cases from the following: NLP, LLMs, and Recommendation engines. Solid software engineering skills (i.e., ability to produce well-structured modules, not only notebook scripts). Python expertise, Docker. English level - strong Intermediate. Excellent communication and problem-solving skills. Will be a plus: Practical experience with cloud platforms (AWS stack is preferred, e.g. Amazon SageMaker, ECR, EMR, S3, AWS Lambda). Practical experience with deep learning models. Experience with taxonomies or ontologies. Practical experience with machine learning pipelines to orchestrate complicated workflows. Practical experience with Spark/Dask, Great Expectations. Responsibilities: Create ML models from scratch or improve existing models. Collaborate with the engineering team, data scientists, and product managers on production models. Develop experimentation roadmap. Set up a reproducible experimentation environment and maintain experimentation pipelines. Monitor and maintain ML models in production to ensure optimal performance. Write clear and comprehensive documentation for ML models, processes, and pipelines. Stay updated with the latest developments in ML and AI and propose innovative solutions.

Summary As a Data Scientist you will build and deploy data-driven solutions to support business goals. You will use your skills in data analytics, machine learning (supervised and unsupervised) and GenAI, to translate complex data into actionable insights. As a data scientist you will work closely with cross-functional team of data engineers, product owners, Devops and bridge the gap between technical implementation and business needs. Responsibilities (Other duties may be assigned. )Experiment and feature engineer with data to design and build machine/deep learning models with appropriate precision, recall, F1 scores to meet the use case nee dPrompt enginee r to develop new and enhance existing Gen-AI applications. (Chatbots, RAG) .Develop and implement advanced AI agents capable of performing autonomous tasks, decision-making, and executing requirement-specific workflows .Document and create experiment reports on the implementation and code in a way that is clear and accessible to both technical and non-technical team members .Perform advanced data analysis, manipulation, and cleansing to extract actionable insights from structured and unstructured data .Create scalable and efficient recommendation systems that enhance user personalization and engagement .Effectively communicate technical solutions and findings to both technical and non-technical stakeholders .Design and deploy AI-driven chatbots and virtual assistants, focusing on natural language understanding and contextual relevance .Implement and optimize supervised and unsupervised learning models for NLP tasks, including text classification, sentiment analysis, and language generation .Explore, understand, and develop state-of-the-art technologies for AI agents, integrating them with broader enterprise systems .Collaborate with cross-functional teams to gather business requirements and deliver AI-driven solutions tailored to specific use cases .Automate workflows using advanced AI tools and frameworks to increase efficiency and reduce manual interventions .Stay informed about cutting-edge advancements in AI, machine learning, NLP, and Gen AI applications, and assess their relevance to the organization . Education and/or experienc e:At least 5 years of experience working with data sciences. Preferably with a bachelors (OR Master) degree in Computer Science, Data Science, or Artificial Intelligenc e. Knowledge Skills and Abiliti es:Strong understanding of mathematics including vector algebra and probability theory for understanding and explaining machine learning (discriminative and generative) mode ls.Strong expertise in data analytics, pattern recognition, machine learning, including predictive modeling and recommendation syste ms.Excellent communication & documentation skills to articulate complex ideas to diverse audienc es.Hands-on experience with large datasets and using distributed systems for analytics and modelli ng.Advanced understanding of natural language processing (NLP) techniques and tools, including transformers like BERT, GPT, or similar models including open-source LL Ms.Strong knowledge of cloud platforms (AWS) for deploying and scaling AI mode ls.Proficiency with code versioning platforms like CodeCommit and GitH ub. Technical Ski lls:Python proficiency and hands-on experience with libraries like (Pandas, Dask, Numpy, Matplotlib, NLTK, Sklearn, Pytorch and Tensorfl ow).Experience in prompt engineering for AI models to enhance functionality and adaptabil ity.Familiarity with AI agent frameworks like LangChain, OpenAI APIs, or other agent-building to ols.Advanced skills in Relational databases [Postgres], Vector Database, querying, analytics, semantic search, and data manipulat ion.Strong problem-solving and critical-thinking skills, with the ability to handle complex technical challen ges.Hands-on experience working with API frameworks like Flask, FastAPI, etc.Proficiency with code versioning platforms like CodeCommit and Git Hub. Prefe rred:Hands-on experience building and deploying conversational AI, chatbots, and virtual assist ants.Familiarity with MLOps pipelines and CI/CD for AI/ML workf lows.Experience with reinforcement learning or multi-agent sys tems. Language SkillsAbility to speak the English language proficiently, both verbally and in wr iting. Work Envir onment The work environment characteristics described here are representative of those an employee encounters while performing the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential fun ctions. Employee works primarily in a home office envi ronment.The home office must be a well-defined work area, separate from normal domestic activity and complete with all essential technology including, but not limited to; separate phone, scanner, printer, computer, etc. as required in order to effectively perform their duties.Compliance with all relevant FINEOS Global policies and procedures related to Quality, Security, Safety, Business Continuity, and Environmental systems.Travel and fieldwork, including international travel may be required. Therefore, employee must possess, or be able to acquire a valid p assport.Must be legally eligible to work in the country in which you ar e hired. FINEOS is an Equal Opportunity Employer. FINEOS does not discriminate on the basis of race, religion, color, sex, gender identity, sexual orientation, age, non-disqualifying physical or mental disability, national origin, veteran status or any other basis covered by appropriate law. All employment is decided on the basis of qualifications, merit, and busin ess need.

Transport is at the core of modern society. Imagine using your expertise to shape sustainable transport and infrastructure solutions for the future? If you seek to make a difference on a global scale, working with next-gen technologies and the sharpest collaborative teams, then we could be a perfect match. About Excelher program: Are you looking for an opportunity to restart your career? Do you want to work with an organization that would value your experience no matter when you gained it? How about working with the best minds in the transportation industry where we need more women power? We are pleased to launch the ExcelHer program – the career returnship program at Volvo Group in India. The program is for women who have been on a career break for a year or more. This is our step towards empowering women to relaunch their professional journey after their absence from the workplace due to personal commitments. Exciting work assignments have been identified which you can refer to in the list below. The assignments are for a tenure of 9 months. The participant of this program would have access to professional development programs, mentoring assistance by a business leader, apart from the experience of working with people from different functions/technologies/culture. Go ahead and apply if you find the opportunities in line with your experience and career interest. Data Scientist: Digital Operations BLR About The Team We, at GTT Digital Operations, enable data availability for vehicle development across all domains by fostering close collaboration between various engineering teams. We are a driving force for developing the way we use and handle our data. We are seeking a Data Engineer with experience in Python and SQL , and advanced data handling techniques. In this role, you will design, build, and optimize data pipelines, manage large datasets efficiently, and create interactive data applications and visualizations to support data-driven decision-making. Skills Required Proficiency in Python for data processing, automation, and visualization. Strong knowledge of SQL for database design, querying, and optimization. Familiarity with Apache Airflow will be an added advantage Education & Experience Required Bachelor’s/Master’s degree in electronics, software, computer engineering, or related field. 5+ years of experience in working with Data Engineering Projects using Python Good understanding and hands-on experience in working with GitHub or similar Excellent leadership, communication, and interpersonal skills, with the ability to collaborate effectively with cross-functional teams and stakeholders. Strong analytical and problem-solving abilities, with a proactive approach to identifying and addressing product risks and challenges. Key Responsibilities Manage and process large datasets efficiently using data chunking and memory-optimized data manipulation techniques in Python with libraries such as Pandas, Polars, Dask, PyArrow, PySpark etc. Work with PostgreSQL databases and write efficient and scalable queries for data extraction, manipulation, and analysis. Automate data workflows and orchestrate processes using Apache Airflow. Handle various file formats including Parquet, JSON, CSV etc for efficient data storage, processing, and sharing. Utilize Amazon S3 Buckets for scalable data storage and efficient data retrieval. Develop and maintain interactive, user-friendly data applications for internal stakeholders. Create insightful visualizations and plots using Python libraries such as Matplotlib, Plotly etc. to communicate data trends and insights. Collaborate with Data Analysts, Data Scientists, and Business Stakeholders to gather and fulfil analytical requirements. We value your data privacy and therefore do not accept applications via mail. Who We Are And What We Believe In We are committed to shaping the future landscape of efficient, safe, and sustainable transport solutions. Fulfilling our mission creates countless career opportunities for talents across the group’s leading brands and entities. Applying to this job offers you the opportunity to join Volvo Group . Every day, you will be working with some of the sharpest and most creative brains in our field to be able to leave our society in better shape for the next generation. We are passionate about what we do, and we thrive on teamwork. We are almost 100,000 people united around the world by a culture of care, inclusiveness, and empowerment. Group Trucks Technology are seeking talents to help design sustainable transportation solutions for the future. As part of our team, you’ll help us by engineering exciting next-gen technologies and contribute to projects that determine new, sustainable solutions. Bring your love of developing systems, working collaboratively, and your advanced skills to a place where you can make an impact. Join our design shift that leaves society in good shape for the next generation.

Required Skills And Qualifications. 7+ years of experience as a Python Developer with a strong portfolio of projects. In-depth understanding of the Python software development stacks, ecosystems, frameworks and tools such as Numpy, Scipy, Pandas, Dask, spaCy, NLTK, sci-kit-learn and PyTorch. Experience with front-end development using HTML, CSS, and JavaScript. Familiarity with database technologies such as SQL and NoSQL. Excellent problem-solving ability with solid communication and collaboration skills. Preferred Skills And Qualifications Experience with popular Python frameworks such as Django, Flask or Pyramid. Knowledge of data science and machine learning concepts and tools. A working understanding of cloud platforms such as AWS, Google Cloud or Azure. Contributions to open-source Python projects or active involvement in the Python community.

Artificial Intelligence Advancement Center is looking for professionals experienced in NLP/LLM/GenAI, who are hands-on and can employ many NLP/Prompt engineering techniques from traditional statistical/ML NLP to DL-based sequence models and transformers in their day-to-day work. Description: You'll be working alongside leading technical experts from all around the world, on a variety of products involving Sequence/token classification, QA/chatbots, translation, semantic/search and summarization, among others. Responsibilities: Design NLP/LLM/GenAI applications/products by following robust coding practices, Explore SoTA models/techniques so that they can be applied for automotive industry usecases Conduct ML experiments to train/infer models; if need be, build models that abide by memory & latency restrictions, Deploy REST APIs or a minimalistic UI for NLP applications using Docker and Kubernetes tools. Showcase NLP/LLM/GenAI applications in the best way possible to users through web frameworks (Dash, Plotly, Streamlit, etc.,) Converge multibots into super apps using LLMs with multimodalities. Develop agentic workflow using Autogen, Agentbuilder, langgraph Build modular AI/ML products that could be consumed at scale. Qualifications: Education : Bachelor’s or master’s degree in computer science, Engineering, Maths or Science Performed any modern NLP/LLM courses/open competitions is also welcomed. Technical Requirements : Soft Skills : Strong communication skills and do excellent teamwork through Git/slack/email/call with multiple team members across geographies. GenAI Skills : Experience in LLM models like PaLM, GPT4, Mistral (open-source models), Work through the complete lifecycle of Gen AI model development, from training and testing to deployment and performance monitoring. Developing and maintaining AI pipelines with multimodalities like text, image, audio etc. Have implemented in real-world Chat bots or conversational agents at scale handling different data sources. Experience in developing Image generation/translation tools using any of the latent diffusion models like stable diffusion, Instruct pix2pix. Expertise in handling large scale structured and unstructured data. Efficiently handled large-scale generative AI datasets and outputs. ML/DL Skills : High familiarity in the use of DL theory/practices in NLP applications Comfort level to code in Huggingface, LangChain, Chainlit, Tensorflow and/or Pytorch, Scikit-learn, Numpy and Pandas Comfort level to use two/more of open source NLP modules like SpaCy, TorchText, fastai.text, farm-haystack, and others NLP Skills : Knowledge in fundamental text data processing (like use of regex, token/word analysis, spelling correction/noise reduction in text, segmenting noisy unfamiliar sentences/phrases at right places, deriving insights from clustering, etc.,) Have implemented in real-world BERT/or other transformer fine-tuned models (Seq classification, NER or QA) from data preparation, model creation and inference till deployment. Python Project Management Skills Familiarity in the use of Docker tools, pipenv/conda/poetry env Comfort level in following Python project management best practices (use of setup.py, logging, pytests, relative module imports,sphinx docs,etc.,) Familiarity in use of Github (clone, fetch, pull/push,raising issues and PR, etc.,) Cloud Skills and Computing : Use of GCP services like BigQuery, Cloud function, Cloud run, Cloud Build, VertexAI, Good working knowledge on other open-source packages to benchmark and derive summary. Experience in using GPU/CPU of cloud and on-prem infrastructures. Skillset to leverage cloud platform for Data Engineering, Big Data and ML needs. Deployment Skills : Use of Dockers (experience in experimental docker features, docker-compose, etc.,) Familiarity with orchestration tools such as airflow, Kubeflow Experience in CI/CD, infrastructure as code tools like terraform etc. Kubernetes or any other containerization tool with experience in Helm, Argoworkflow, etc., Ability to develop APIs with compliance, ethical, secure and safe AI tools. UI : Good UI skills to visualize and build better applications using Gradio, Dash, Streamlit, React, Django, etc., Deeper understanding of javascript, css, angular, html, etc., is a plus. Miscellaneous Skills : Data Engineering: Skillsets to perform distributed computing (specifically parallelism and scalability in Data Processing, Modeling and Inferencing through Spark, Dask, RapidsAI or RapidscuDF) Ability to build python-based APIs (e.g.: use of FastAPIs/ Flask/ Django for APIs) Experience in Elastic Search and Apache Solr is a plus, vector databases. If interested, please share update CV with Comp details and NP and Current location along with exp in below skills NLP/LLM/GenAI applications/products SoTA models/techniques Experience in LLM models like PaLM, GPT4, Mistral (open-source models) ML/DL Skills NLP Skills GCP services like BigQuery, Cloud function, Cloud run, Cloud Build, VertexAI