524 Mlops Jobs - Page 14

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

We're Hiring: AI/ML Consultant Location: Vijayawada Experience: 510 Years | Type: Full-Time | Industry: Artificial Intelligence, Machine Learning, Data Science Do you have a passion for building intelligent systems that solve real-world problems? Are you skilled in deploying scalable AI/ML models that create business impact? If yes, we’d love to hear from you! Role Overview We are looking for an AI/ML Consultant to develop and integrate intelligent solutions that drive automation, decision-making, and innovation across multiple domains. Key Responsibilities Design and deploy AI/ML models for data-driven solutions Optimize models for scalability, accuracy, and interpretability Integrate AI models with low-code/no-code platforms for business users Work with stakeholders to define AI use cases and encourage adoption Required Skills Proficient in Python, R, and frameworks such as TensorFlow, PyTorch, Scikit-learn Experience in MLOps, model deployment, CI/CD, and version control Knowledge of ethical AI, bias detection, and explainable AI techniques Hands-on experience with AI-driven automation, chatbots, and decision support systems Preferred Certifications Certified AI/ML Engineer (AWS, Google Cloud, Microsoft) TensorFlow Developer Certificate Certification in AI Ethics & Governance

Role Overview: We are looking for a highly experienced MLOps and ML Engineer to lead the design, deployment, and optimization of machine learning systems at scale. This role requires deep expertise in MLOps practices, CI/CD automation, and AWS SageMaker, with a strong foundation in machine learning engineering and cloud-native development. Key Responsibilities: Architect and implement robust MLOps pipelines for model development, deployment, monitoring, and governance. Lead the operationalization of ML models using AWS SageMaker and other AWS services. Build and maintain CI/CD pipelines for ML workflows using tools like GitHub Actions, Jenkins, or AWS CodePipeline. Automate model lifecycle management including retraining, versioning, and rollback. Collaborate with data scientists, ML engineers, and DevOps teams to ensure seamless integration and scalability. Monitor production models for performance, drift, and reliability. Establish best practices for reproducibility, security, and compliance in ML systems. Required Skills: 10+ years of experience in ML Engineering, MLOps, or related fields. Deep hands-on experience with AWS SageMaker, Lambda, S3, CloudWatch, and related AWS services. Strong programming skills in Python and experience with Docker, Kubernetes, and Terraform. Expertise in CI/CD tools and infrastructure-as-code. Familiarity with model monitoring tools (e.g., Evidently, Prometheus, Grafana). Solid understanding of ML algorithms, data pipelines, and production-grade systems. Preferred Qualifications: AWS Certified Machine Learning Specialty or DevOps Engineer certification. Experience with feature stores, model registries, and real-time inference systems. Leadership experience in cross-functional ML/AI teams. Primary Skills: MLOps, ML Engineering, AWS related services (SageMaker/S3/CloudWatch)

Key Responsibilities : - Conduct feature engineering, data analysis, and data exploration to extract valuable insights. - Develop and optimize Machine Learning models to achieve high accuracy and performance. - Design and implement Deep Learning models, including Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), and Reinforcement Learning techniques. - Handle real-time imbalanced datasets and apply appropriate techniques to improve model fairness and robustness. - Deploy models in production environments and ensure continuous monitoring, improvement, and updates based on feedback. - Collaborate with cross-functional teams to align ML solutions with business goals. - Utilize fundamental statistical knowledge and mathematical principles to ensure the reliability of models. - Bring in the latest advancements in ML and AI to drive innovation. Requirements : - 4-5 years of hands-on experience in Machine Learning and Deep Learning. - Strong expertise in feature engineering, data exploration, and data preprocessing. - Experience with imbalanced datasets and techniques to improve model generalization. - Proficiency in Python, TensorFlow, Scikit-learn, and other ML frameworks. - Strong mathematical and statistical knowledge with problem-solving skills. - Ability to optimize models for high accuracy and performance in real-world scenarios. Preferred Qualifications : - Experience with Big Data technologies (Hadoop, Spark, etc.) - Familiarity with containerization and orchestration tools (Docker, Kubernetes). - Experience in automating ML pipelines with MLOps practices. - Experience in model deployment using cloud platforms (AWS, GCP, Azure) or MLOps tools.

Work closely with cross-functional teams of business partners, data scientists, data engineers, solutions, and data architects to quickly deliver scalable Artificial Intelligence (AI) solutions including DL, ML, NLP, optimization etc. Development, deployment, and maintenance of scalable AI solutions including the optimization of data queries, code refactoring, shared library usage and documentation of process and solution artifacts. Evolving, implementing, and advocating a global MLOps model across project teams to ensure standardization and consistency of AI solutions design, R&D though deployment and maintenance. Design and implement automation processes during each MLOps project phase ensuring reusable code library development, testing & adoption. Partner with business stakeholders and AI project teams to clearly understand business requirements; design and develop scalable AI solutions and promotion of their benefits back to business partners with an emphasis on value. Become a Subject Matter Expert (SME) on best usage of the teams global data science platform driving MLOps processes and reducing the cost of scalable AI delivery for business partners. Research and promote latest technologies, design patterns and best practice delivery models that drive optimal business value and ensure continuous improvement of team, processes, and platforms. Employ innovative thinking across everything you do, always seeking the best ways of working for our teams and business partners

My profile - linkedin.com/in/yashsharma1608 Position : AI Architect ( Gen AI ) Experience : 8 - 10 years Notice Period : Immediate to 15 days. Budget upto - 45 to 50 LPA Location : Bangalore. Note : - (any developer with minimum 3 to 4 years into AI), SaaS company mandatory. Product Based company Mandatory Discuss the feasibility of AI/ML use cases along with architectural design with business teams and translate the vision of business leaders into realistic technical implementation Play a key role in defining the AI architecture and selecting appropriate technologies from a pool of open-source and commercial offerings Design and implement robust ML infrastructure and deployment pipelines Establish comprehensive MLOps practices for model training, versioning, and deployment Lead the development of HR-specialized language models (SLMs) Implement model monitoring, observability, and performance optimization frameworks Develop and execute fine-tuning strategies for large language models Create and maintain data quality assessment and validation processes Design model versioning systems and A/B testing frameworks Define technical standards and best practices for AI development Optimize infrastructure for cost, performance, and scalability Required Qualifications 7+ years of experience in ML/AI engineering or related technical roles 3+ years of hands-on experience with MLOps and production ML systems Demonstrated expertise in fine-tuning and adapting foundation models Strong knowledge of model serving infrastructure and orchestration Proficiency with MLOps tools (MLflow, Kubeflow, Weights & Biases, etc.) Experience implementing model versioning and A/B testing frameworks Strong background in data quality methodologies for ML training Proficiency in Python and ML frameworks (PyTorch, TensorFlow, Hugging Face) Experience with cloud-based ML platforms (AWS, Azure, Google Cloud) Proven track record of deploying ML models at scale Preferred Qualifications Experience developing AI applications for enterprise software domains Knowledge of distributed training techniques and infrastructure Experience with retrieval-augmented generation (RAG) systems Familiarity with vector databases (Pinecone, Weaviate, Milvus) Understanding of responsible AI practices and bias mitigation Bachelor's or Master's degree in Computer Science, Machine Learning, or related field What We Offer Opportunity to shape AI strategy for a fast-growing HR technology leader Collaborative environment focused on innovation and impact Competitive compensation package Professional development opportunities Flexible work arrangements Qualified candidates who are passionate about applying cutting-edge AI to transform HR technology are encouraged to apply

RESPONSIBILITIES: Develop, productionize, and deploy scalable, resilient software solutions for operationalizing AI & ML. Deploy Machine Learning (ML) models and Large Language Models (LLM) securely and efficiently, both in the cloud and on-premises, using state of the art platforms, tools, and techniques. Provide effective model observability, monitoring, and metrics by instrumenting logging, dashboards, alerts, etc. In collaboration with Data Engineers, design and build pipelines for extraction, transformation, and loading of data from a variety of data sources for AI & ML models as well as RAG architectures for LLMs. Enable Data Scientists to work more efficiently by providing tools for experiment tracking and test automation. Ensure scalability of built solutions by developing and running rigorous load tests. Facilitate integration of AI & ML capabilities into user experience by building APIs, UIs, etc. Stay current on new developments in AI & ML frameworks, tools, techniques, and architectures available for solution development, both private and open source. Coach data scientists and data engineers on software development best practices to write scalable, maintainable, well-designed code. Agile Project Work Work in cross-functional agile teams of highly skilled software/machine learning engineers, data scientists, DevOps engineers, designers, product managers, technical delivery teams, and others to continuously innovate AI and MLOps solutions. Act as a positive champion for broader organization to develop stronger understanding of software design patterns that deliver scalable, maintainable, well-designed analytics solutions. Advocate for security and responsibility best practices and tools. Acts as an expert on complex technical topics that require cross-functional consultation. Perform other duties as required. QUALIFICATIONS: Experience applying continuous integration/continuous delivery best practices, including Version Control, Trunk Based Development, Release Management, and Test-Driven Development Experience with popular MLOps tools (e.g., Domino Data Labs, Dataiku, mlflow, AzureML, Sagemaker), and frameworks (e.g.: TensorFlow, Keras, Theano, PyTorch, Caffe, etc.) Experience with LLM platforms (OpenAI, Bedrock, NVAIE) and frameworks (LangChain, LangFuse, vLLM, etc.) Experience in programming languages common to data science such as Python, SQL, etc. Understanding of LLMs, and supporting concepts (tokenization, guardrails, chunking, Retrieval Augmented Generation, etc.). Knowledge of ML lifecycle (wrangling data, model selection, model training, modeling validation and deployment at scale) and experience working with data scientists Familiar with at least one major cloud provider (Azure, AWS, GCP), including resource provisioning, connectivity, security, autoscaling, IaC. Familiar with cloud data warehousing solutions such as Snowflake, Fabric, etc. Experience with Agile and DevOps software development principles/methodologies and working on teams focused on delivering business value. Experience influencing and building mindshare convincingly with any audience. Confident and experienced in public speaking. Ability to communicate complex ideas in a concise way. Fluent with popular diagraming and presentation software. Demonstrated experience in teaching and/or mentoring professionals.

We are seeking an experienced professional in AI and machine learning with a strong focus on large language models (LLMs) for a 9-month project The role involves hands-on expertise in developing and deploying agentic systems and working with transformer architectures, fine-tuning, prompt engineering, and task adaptation Responsibilities include leveraging reinforcement learning or similar methods for goal-oriented autonomous systems, deploying models using MLOps practices, and managing large datasets in production environments The ideal candidate should excel in Python, ML libraries (Hugging Face Transformers, TensorFlow, PyTorch), data engineering principles, and cloud platforms (AWS, GCP, Azure) Strong analytical and communication skills are essential to solve complex challenges and articulate insights to stakeholders

We are seeking a highly skilled and innovative Data Scientist with a strong focus on Generative AI, NLP, and Large Language Models (LLMs). The ideal candidate will design, develop, and deploy end-to-end data science solutions that harness the power of advanced ML, deep learning, and Gen AI technologies to drive real-world impact. Key Responsibilities: Design and implement scalable data science solutions using Generative AI, NLP, and ML techniques. Develop, fine-tune, and evaluate Large Language Models (LLMs) such as GPT, BERT, and similar architectures. Analyze structured and unstructured data to generate actionable insights for business problems. Collaborate cross-functionally with engineering, product, and business teams to integrate AI models into production systems. Conduct cutting-edge research in Gen AI and deep learning; evaluate and apply recent advancements. Build and maintain robust pipelines for model training, evaluation, monitoring, and deployment. Communicate complex technical findings clearly to technical and non-technical stakeholders. Required Skills & Qualifications: Strong hands-on experience with Generative AI, LLMs (e.g., OpenAI, Hugging Face Transformers, etc.). Proven expertise in core NLP tasks: text classification, summarization, named entity recognition, sentiment analysis, etc. Proficient in Python and related libraries: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch. Experience developing, validating, and deploying ML models in production environments. Familiarity with vector databases (e.g., FAISS, Pinecone), embeddings, and semantic search. Exposure to cloud platforms like AWS, Azure, or GCP, and MLOps tools/workflows (CI/CD, model monitoring, etc.). Preferred Qualifications: Experience in prompt engineering, Retrieval-Augmented Generation (RAG), or fine-tuning/customizing LLMs. Contributions to AI research papers, open-source projects, or participation in ML competitions (e.g., Kaggle) is a plus. Knowledge of responsible AI practices, model interpretability, and bias mitigation techniques.

Role & responsibilities: Looking for 5 to 8 years experience of ML Engineer with strong Azure Cloud DevOps with even stronger DABs DevOps skills with even stronger DABs Databricks Asset Bundles implementation knowledge. 1 Azure Cloud Engineer 2 Azure DevOps CICD experienced in DABs 3 ML Engineer for deployment Translate business requirement into technical solution Implementation of MLOPS Scalable solution using AIML and reduce the risk of Fraud and other fiscal crisis Creating MLOPS Architecture and implementing it for multiple models in a scalable and automated way Designing and implementing end to end ML solutions Operationalize and monitor machine learning models using high end tools and technologies Design implementation of DevOps principles in Machine Learning Data Science quality assurance and testing Collaborate with data scientists engineers and other key stakeholders Preferred candidate profile: Azure Cloud Engineering Design implement and manage scalable cloud infrastructure on Microsoft Azure Ensure high availability performance and security of cloud based applications Collaborate with cross functional teams to define and implement cloud solutions Azure DevOps CICD Develop and maintain CICD pipelines using Azure DevOps Automate deployment processes to ensure efficient and reliable software delivery Monitor and troubleshoot CICD pipelines to ensure smooth operation DABs Databricks Asset Bundles Implementation Lead the implementation and management of Databricks Asset Bundles Optimize data workflows and ensure seamless integration with existing systems Provide expertise in DABs to enhance data processing and analytics capabilities Machine Learning Deployment Deploy machine learning models into production environments Monitor and maintain ML models to ensure optimal performance Collaborate with data scientists and engineers to integrate ML solutions into applications.

Job Title: Senior MLOps Engineer Location : Pan India (WFO) Experience Range: 8-12 years Primary Key skills: MLOps Key Responsibilities: Design, develop and maintain end-to-end MLOps pipelines for model deployment, monitoring, maintenance, and scalability. Automate the retraining, testing, and validation processes for ML models. Collaborate with cross-functional teams, including data science, software engineering and DevOps, to integrate ML models into production systems. Monitor model performance, diagnose issues, and implement improvements. Ensure scalability, reliability, and compliance of ML systems in production. Optimize infrastructure costs while maintaining high system performance. Stay up-to-date with the latest developments in MLOps, machine learning and AI, and apply new techniques to improve existing models and processes. Qualifications: Education: Bachelors or Master’s degree in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field. Experience: Solid experience as a MLOps Engineer, Machine Learning Engineer, DevOps Engineer, or similar role. Experience in the retail industry or e-commerce is highly desirable. Technical Skills: Strong experience with Infrastructure as Code frameworks and languages (Terraform, Bicep or ARM) Strong programming skills in Python and experience with ML frameworks (TensorFlow, PyTorch, etc.). Hands-on experience with containerization (Docker) and orchestration tools (Kubernetes). Proficiency in CI/CD tools and cloud platforms (AWS, Azure, or Google Cloud). Knowledge of model monitoring and evaluation metrics. Familiarity with version control systems, such as Git, and model versioning tools like MLflow or DVC. Experience with Generative AI product deployment is desirable. Experience with big data using Databricks, Snowflake, Apache Spark or Hadoop is desirable. – some of these System level architecture understanding including scaling, MLOps, model/data monitoring, andensuring a deterministic pipeline. Soft Skills: Strong problem-solving skills with the ability to work independently and collaboratively in a fast-paced environment. Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders. A proactive attitude and a passion for continuous learning and innovation. Role & responsibilities Preferred candidate profile

Role & responsibilities Mandatory skill : MLOps with Azure Databricks / Devops A person who has exposure to Models/MLOps eco-system having exposure to Model Life Cycle Management, with primary responsibility being ability to engage with stakeholders around requirements elaboration, having them broken into stories for the pods by engaging with architects/leads for designs, participation in UAT and creation of user scenario testings, creation of product documentation describing features, capabilities etc. we basically not looking for a Project Manager who will track things. Preferred candidate profile

Role Summary: The AI Manager leads the AI team, contributing to technical strategy and decision-making. This role involves managing the day-to-day delivery of the AI/ML team, responsible for building, deploying, and maintaining robust, scalable, and efficient ML models. Essential Responsibilities: Provide technical leadership and strategic direction for the AI team, aligning with overall company objectives. Lead and mentor a team of data engineers, MLOps engineers, and machine learning engineers to achieve project objectives and deliverables (development and deployment of ML models for AI use cases across the platform). Contribute to AI/ML architecture design and implementation. Collaborate with business, engineering, infrastructure, and data science teams to translate their needs or challenges into production-grade Artificial Intelligence and Machine Learning models for batch and real-time requirements. Serve as a primary point of contact for US & Thryve leaders, ensuring clear communication and aligning with project goals and objectives. Guide the AI/ML engineers with respect to business objectives, connecting the dots, and helping the team come up with optimal ML models based on use cases. Manage day-to-day operations, ensuring adherence to process, scope, quality, and timelines. Assume the role of an engagement coordinator between the Thryve India team and US counterparts (techno-functional). Highlight risks and concerns to the leadership team and work with multiple stakeholders to establish alignment, contingency, and/or mitigation plans as required. Proactively track delivery statuses and project progress, taking ownership of all deliverables from Thryve. Communicate effectively with team members, US stakeholders, and management. Stay up-to-date with the latest trends and advancements in AI and ML, and identify opportunities for the team to implement new models and technologies. Propose and implement best engineering and research practices for scaling ML-powered features, enabling fast iteration and efficient experimentation with novel features. Required Skills and Qualifications: Bachelor's degree in engineering, computer science, or a related field. 12 to 16 years of total experience, with at least 3 years of experience leading/managing AI/ML projects (Worked in Predictive modeling projects), achieving clear & measurable business objectives. Good understanding of US Health insurance with at least 2+ years of experience in the same. Experience and understanding of the entire MLOps pipeline, from data ingestion to production. Strong understanding of AI/ML concepts & algorithms, including supervised, unsupervised, and reinforcement learning, as well as MLOps. Proven ability to take successful, complex ideas from experimentation to production. Hands-on experience with Google Cloud Platform (GCP) or any public cloud platform. Excellent communication, presentation, and interpersonal skills. Proven experience in leading, motivating & inspiring teams to foster collaboration. Proficient in identifying, assessing, and calling out risks and mitigation plans to stakeholders. Experience in implementing process improvements and learning from past projects to enhance future performance. Ability to analyze external and internal processes and create strategies for service delivery optimization. Experience in working with diverse teams and stakeholders, understanding their needs and managing expectations. Experience In handling team & managing multiple stakeholders.

We are seeking a skilled and motivated individual to join our team. The ideal candidate will have experience working with Microsoft CoPilot Studio and Azure Communication Framework, with a passion for building scalable and reliable solutions. Key Responsibilities Design, develop, and implement custom applications using CoPilot Studio, ensuring alignment with business requirements. Integrate and maintain communication tools and services utilizing the Azure Communication Framework. Collaborate with product owners and technical leads to design effective workflows and solutions. Develop, test, and deploy end-to-end solutions, ensuring scalability, reliability, and maintainability. Troubleshoot, debug, and optimize applications for maximum performance and functionality. Conduct code reviews and follow best practices for code quality, version control, and testing. Stay updated with the latest trends and developments in Microsoft CoPilot, Azure technologies, and other relevant tools to enhance solutions.

Role & responsibilities We are looking for MLOps/ML Engineer with Dataiku DSS platform with MNC company for permanent position, Remote. Preferred candidate profile We are seeking a skilled MLOps/ML Engineer to serve as our subject matter expert for Dataiku DSS. In this pivotal role, you will manage and scale our end-to-end machine learning operations, all of which are built on the Dataiku platform. Key responsibilities include designing automated data pipelines, deploying models as production APIs, ensuring the reliability of scheduled jobs, and championing platform best practices. Extensive, proven experience with Dataiku is mandatory. Data Pipeline Development: Design and implement Extract, Transform, Load (ETL) processes to collect, process, and analyze data from diverse sources. Workflow Optimization: Develop, configure, and optimize Dataiku DSS workflows to streamline data processing and machine learning operations. Integration: Integrate Dataiku DSS with cloud platforms (e.g., AWS, Azure, Google Cloud Platform) and big data technologies such as Snowflake, Hadoop, and Spark. AI/ML Model development & Implementation: Implement and optimize machine learning models within Dataiku for predictive analytics and AI-driven solutions. MLOps & Data Ops: Deployment of data pipelines and AI/ML models within the Dataiku platform. Dataiku Platform Management: Build, Manage & Support Dataiku platform. Automation: Automate data workflows, monitor job performance, and ensure scalable execution. Customization: Develop and maintain custom Python/R scripts within Dataiku to enhance analytics capabilities. Dataiku Project Management: Develop and maintain custom Python/R scripts within Dataiku to enhance analytics capabilities. Required Skills and Qualifications: Experience Level: 2 to 6 years of hands-on experience with Dataiku DSS platform and data engineering. Educational Background: Bachelors or Master’s degree in Computer Science, Data Science, Information Technology, or a related field. Technical Proficiency: Experience with Dataiku DSS platform. Strong programming skills in Python and SQL. Familiarity with cloud services (AWS, Azure, GCP) and big data technologies (Hadoop, Spark). Analytical Skills: Ability to analyze complex data sets and provide actionable insights. Problem-Solving: Strong troubleshooting skills to address and resolve issues in data workflows and models. Communication: Effective verbal and written communication skills to collaborate with team members and stakeholders.

We are seeking a MLOps Engineer to design, implement, and manage scalable machine learning infrastructure and automation pipelines. The ideal candidate will have deep hands-on expertise in Azure, AKS, Infrastructure as Code, and CI/CD, with a passion for enabling efficient and reliable deployment of machine learning models in production environments. Responsibilities:- Architect & Deploy: Design and manage scalable ML infrastructure on Azure (AKS), leveraging Infrastructure as Code principles. Automate & Accelerate: Build and optimize CI/CD pipelines with GitHub Actions for seamless software, data, and model delivery. Engineer Performance: Develop efficient and reliable data pipelines using Python and distributed computing frameworks. Ensure Reliability : Implement solutions for deploying and maintaining ML models in production. Collaborate & Innovate : Partner with data scientists and engineers to continuously enhance existing MLOps capabilities. Expertise:- Azure & AKS : Deep hands-on experience. IaC & CI/CD : Mastery of Terraform/Bicep & GitHub Actions. Data Engineering : Advanced Python & Spark for complex pipelines. ML Operations : Proven ability in model serving & monitoring. Problem Solver: Adept at navigating complex technical challenges and delivering solutions. Nice to Have:- Experience with Kubeflow, MLflow, or similar MLOps tools. Exposure to other cloud platforms (AWS, GCP) in addition to Azure. Familiarity with security, compliance, and cost-optimization for ML workloads.

Dun & Bradstreet Technology and Corporate Services India LLP is looking for candidates for the role of Senior Analyst to support the Data Science team. The candidate needs to work closely with the team based in India and across a range of Analytics leaders who are located globally to fulfill the deliverables on a timely basis. Role & responsibilities Work across range of Analytical Solutions that includes Implementation, Testing, Validation, Documentation, Monitoring and Reporting. Extracting large data from various data sources using multiple tools such as Python/ Pyspark /Hadoop/ SQL/ etc., to perform multiple analysis. Creating data pipelines, performing model scoring, and validating model results. Utilize custom built automated frameworks for all deliverables. Work on MLOps frameworks to deploy, monitor and validate ML models in real time. Work with multiple teams such as Data Supply, ML Engineers, Technology to implement analytical solutions and models. Preferred candidate profile: 2 to 4 years of relevant experience in Data Analytics / Data Science roles. Strong programming skill in tools such as Pyspark, Python, SQL to manipulate data and create deployable production code. Knowledge of Python packages and tools. Sound Knowledge and exposure to application of Statistical / Machine Learning Techniques. Experience in building and optimizing data pipelines, architectures and data sets. Ability to interpret and translate data into meaningful business insights. Excellent verbal, written communication and presentation skills.

Job Summary : We are seeking a skilled Data Scientist with expertise in AI orchestration and embedded systems to support a sprint-based Agile implementation focused on integrating generative AI capabilities into enterprise platforms such as Slack, Looker, and Confluence. The ideal candidate will have hands-on experience with Gemini and a strong understanding of prompt engineering, vector databases, and orchestration infrastructure. Key Responsibilities : - Develop and deploy Slack-based AI assistants leveraging Gemini models. - Design and implement prompt templates tailored to enterprise data use cases (Looker and Confluence). - Establish and manage an embedding pipeline for Confluence documentation. - Build and maintain orchestration logic for prompt execution and data retrieval. - Set up API authentication and role-based access controls for integrated systems. - Connect and validate vector store operations (e.g., Pinecone, Weaviate, or Snowflake vector extension). - Contribute to documentation, internal walkthroughs, and user acceptance testing planning. - Participate in Agile ceremonies including daily standups and sprint demos. Required Qualifications : - Proven experience with Gemini and large language model deployment in production environments. - Proficiency in Python, orchestration tools, and prompt engineering techniques. - Familiarity with vector database technologies and embedding workflows. - Experience integrating APIs for data platforms such as Looker and Confluence. - Strong understanding of access control frameworks and enterprise-grade authentication. - Demonstrated success in Agile, sprint-based project environments. Preferred Qualifications : - Experience working with Slack app development and deployment. - Background in MLOps, LLMOps, or AI system orchestration at scale. - Excellent communication skills and ability to work in cross-functional teams.

Job Title : Data Scientist - AI Integration & Orchestration. Job Summary : We are seeking a skilled Data Scientist with expertise in AI orchestration and embedded systems to support a sprint-based Agile implementation focused on integrating generative AI capabilities into enterprise platforms such as Slack, Looker, and Confluence. The ideal candidate will have hands-on experience with Gemini and a strong understanding of prompt engineering, vector databases, and orchestration infrastructure. Key Responsibilities : - Develop and deploy Slack-based AI assistants leveraging Gemini models. - Design and implement prompt templates tailored to enterprise data use cases (Looker and Confluence). - Establish and manage an embedding pipeline for Confluence documentation. - Build and maintain orchestration logic for prompt execution and data retrieval. - Set up API authentication and role-based access controls for integrated systems. - Connect and validate vector store operations (e.g., Pinecone, Weaviate, or Snowflake vector extension). - Contribute to documentation, internal walkthroughs, and user acceptance testing planning. - Participate in Agile ceremonies including daily standups and sprint demos. Required Qualifications : - Proven experience with Gemini and large language model deployment in production environments. - Proficiency in Python, orchestration tools, and prompt engineering techniques. - Familiarity with vector database technologies and embedding workflows. - Experience integrating APIs for data platforms such as Looker and Confluence. - Strong understanding of access control frameworks and enterprise-grade authentication. - Demonstrated success in Agile, sprint-based project environments. Preferred Qualifications : - Experience working with Slack app development and deployment. - Background in MLOps, LLMOps, or AI system orchestration at scale. - Excellent communication skills and ability to work in cross-functional teams.

As the MLOps Lead, you will be responsible for leading the deployment and operationalization of AI/ML solutions. You will collaborate with data scientists, engineers, and IT teams to ensure that machine learning models are efficiently deployed, monitored, and maintained. Your role will be crucial in bridging the gap between data science and production, ensuring that our AI initiatives are scalable, reliable, and deliver business value. Responsibilities: • Lead the design, implementation, and management of MLOps pipelines to deploy machine learning models into production. Work closely with data scientists to understand model requirements and ensure smooth integration and deployment. Develop and maintain infrastructure for model training, validation, deployment, and monitoring. Implement best practices for CI/CD pipelines in the context of AI/ML model development and deployment. Ensure scalability, reliability, and performance of deployed models through monitoring and continuous optimization. Leverage containerization and orchestration tools (e.g., Docker, Kubernetes) to manage model deployment environments. Collaborate with IT and DevOps teams to ensure seamless integration of AI/ML solutions with existing systems. Implement and enforce security best practices for AI/ML models and data pipelines. Stay updated with the latest advancements in MLOps tools, frameworks, and methodologies. Provide technical leadership and mentorship to junior team members. Requirements: • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field. Proven experience (8+ years) in MLOps, DevOps, or a related field, with a focus on deploying machine learning models. Strong understanding of machine learning, deep learning, NLP, and generative AI techniques. Proficiency with MLOps tools and frameworks such as MLflow, Kubeflow, TensorFlow Extended (TFX), or similar. Experience with CI/CD tools such as Jenkins, GitLab CI, or CircleCI. Proficiency in programming languages such as Python and familiarity with ML/DL frameworks like TensorFlow, PyTorch, and scikit-learn. Experience with cloud platforms (AWS, GCP, Azure) and their AI/ML services. Knowledge of containerization and orchestration tools (Docker, Kubernetes). Strong understanding of version control systems (e.g., Git) and collaborative development workflows. Excellent problem-solving skills and the ability to design robust, scalable MLOps solutions. Strong communication skills, with the ability to collaborate effectively with cross-functional teams. Experience in leading and mentoring technical teams. Preferred Qualifications: Experience with big data technologies and tools such as Hadoop, Spark, and Kafka. Familiarity with data visualization tools such as Tableau, Power BI, or similar.

As an AI/ML Solution Architect, you will be responsible for designing and overseeing the architecture of AI and machine learning solutions. You will work closely with data scientists, MLOps team, engineers, and business stakeholders to ensure that our AI/ML initiatives are scalable, robust, and aligned with business objectives. Your expertise in machine learning, deep learning, natural language processing (NLP), and generative AI will be crucial in guiding the development and deployment of advanced AI solutions. Responsibilities: • Design and architect end-to-end AI/ML solutions that meet business requirements and technical specifications. • Develop and implement scalable machine learning and deep learning models, ensuring they integrate seamlessly into existing systems. • Create architectures for NLP applications, including text processing, sentiment analysis, named entity recognition, and language modelling. • Leverage generative AI and large language models (LLMs) to develop innovative applications. • Collaborate with data engineers to design data pipelines and ensure data quality and accessibility. • Provide technical leadership and guidance to data scientists and engineers throughout the development lifecycle. • Evaluate and select appropriate AI/ML tools, frameworks, and technologies to meet project needs. • Ensure best practices in AI/ML model development, including versioning, testing, and validation. • Stay updated with the latest advancements in AI/ML technologies and incorporate them into architectural designs. • Communicate complex AI/ML concepts and solutions effectively to non-technical stakeholders. Requirements: • Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field with proven experience (8+ years) in designing and implementing AI/ML solutions, with a strong focus on machine learning, deep learning, NLP, and generative AI. • Proficiency in deep learning frameworks such as TensorFlow, PyTorch, and Keras and experience with cloud platforms (AWS, GCP, Azure) and their AI/ML services. • Strong expertise in NLP techniques and tools, including experience with LLMs like Gemini, GPT, BERT, etc. • Strong communication skills, with the ability to present technical concepts to both technical and non-technical audiences and experience in leading and mentoring technical teams. • Experience with MLOps practices and tools for continuous integration and deployment of machine learning models.

Mandatory Skills & Experience : - Expertise in designing and optimizing machine-learning operations, with a preference for LLM Ops. - Proficient in Data Science, Machine Learning, Python, SQL, Linux/Unix shell scripting. - Experience on Large Language Models and Natural Language Processing (NLP), and experience with researching, training, and fine-tuning LLMs. - Contribute towards fine-tune Transformer models for optimal performance in NLP tasks, if required. - Implement and maintain automated testing and deployment processes for machine learning models w.r.t LLMOps. - Implement version control, CI/CD pipelines, and containerization techniques to streamline ML and LLM workflows. - Develop and maintain robust monitoring and alerting systems for generative AI models ensuring proactive identification and resolution of issues. - Research or engineering experience in deep learning with one or more of the following : generative models, segmentation, object detection, classification, model optimisations. - Experience implementing RAG frameworks as part of available-ready products. - Experience in setting up the infrastructure for the latest technology such as Kubernetes, Serverless, Containers, Microservices etc. - Experience in scripting/programming to automate deployments and testing, working on tools like Terraform and Ansible. - Scripting languages like Python, bash, YAML etc. - Experience on CI/CD opensource and enterprise tool sets such as Argo CD, and Jenkins (others like Jenkins X, Circle CI, Argo CD, Tekton, Travis, Concourse an advantage). - Experience with the GitHub/DevOps Lifecycle. - Experience in Observability solutions (Prometheus, EFK stacks, ELK stacks, Grafana, Dynatrace, AppDynamics). - Experience in at-least one of the clouds for example Azure/AWS/GCP. - Significant experience on microservices-based, container-based or similar modern approaches of applications and workloads. - You have exemplary verbal and written communication skills (English). - Able to interact and influence at the highest level, you will be a confident presenter and speaker, able to command the respect of your audience. Desired Skills & Experience : - Bachelor level technical degree or equivalent experience; Computer Science, Data Science, or Engineering background preferred; Master's Degree desired. - Experience in LLM Ops or related areas, such as DevOps, data engineering, or ML infrastructure. - Hands-on experience in deploying and managing machine learning and large language model pipelines in cloud platforms (i.e., AWS, Azure) for ML workloads. - Familiar with data science, machine learning, deep learning, and natural language processing concepts, tools, and libraries such as Python, TensorFlow, PyTorch, NLTK etc. - Experience in using retrieval augmented generation and prompt engineering techniques to improve the model's quality and diversity to improve operations efficiency. - Proven experience in developing and fine-tuning Language Models (LLMs). - Stay up-to-date with the latest advancements in Generative AI, conduct research, and explore innovative techniques to improve model quality and efficiency. - The perfect candidate will already be working within a System Integrator, Consulting or Enterprise organisation with 8+ years of experience in a technical role within the Cloud domain. - Deep understanding of core practices including SRE, Agile, Scrum, XP and Domain Driven Design. - Familiarity with the CNCF open-source community. - Enjoy working in a fast-paced and dynamic environment using the latest technologies.

Job Summary : We are seeking a skilled Data Scientist with expertise in AI orchestration and embedded systems to support a sprint-based Agile implementation focused on integrating generative AI capabilities into enterprise platforms such as Slack, Looker, and Confluence. The ideal candidate will have hands-on experience with Gemini and a strong understanding of prompt engineering, vector databases, and orchestration infrastructure. Key Responsibilities : - Develop and deploy Slack-based AI assistants leveraging Gemini models. - Design and implement prompt templates tailored to enterprise data use cases (Looker and Confluence). - Establish and manage an embedding pipeline for Confluence documentation. - Build and maintain orchestration logic for prompt execution and data retrieval. - Set up API authentication and role-based access controls for integrated systems. - Connect and validate vector store operations (e.g., Pinecone, Weaviate, or Snowflake vector extension). - Contribute to documentation, internal walkthroughs, and user acceptance testing planning. - Participate in Agile ceremonies including daily standups and sprint demos. Required Qualifications : - Proven experience with Gemini and large language model deployment in production environments. - Proficiency in Python, orchestration tools, and prompt engineering techniques. - Familiarity with vector database technologies and embedding workflows. - Experience integrating APIs for data platforms such as Looker and Confluence. - Strong understanding of access control frameworks and enterprise-grade authentication. - Demonstrated success in Agile, sprint-based project environments. Preferred Qualifications : - Experience working with Slack app development and deployment. - Background in MLOps, LLMOps, or AI system orchestration at scale. - Excellent communication skills and ability to work in cross-functional teams.

Mandatory Skills : Python, Machine Learning Models, Flask, Data Visualization Tools. Good to have : Gen AI, NLP, Cloud (AWS) etc Notice Period : Immediate - 30 days Responsibilities : - Design, develop, and deploy machine learning models and algorithms. - Perform data analysis to extract insights and identify patterns. - Develop and implement feature engineering processes to enhance model performance. - Collaborate with data scientists and cross-functional teams to understand business requirements and translate them into technical solutions. - Build and maintain scalable data pipelines and infrastructure. - Develop and deploy machine learning applications using Flask, FastAPI, or Django. - Conduct experiments, model tuning, and evaluate model performance using appropriate metrics. Must-Have Skills : - Python and its libraries (NumPy, pandas, scikit-learn, TensorFlow, PyTorch). - Experience with web frameworks such as Flask, FastAPI, or Django. - Strong understanding of machine learning algorithms and techniques. - Experience with data visualization tools (e.g., Matplotlib, Seaborn). - Strong problem-solving skills and ability to work independently as well as in a team. - Excellent communication skills to effectively convey technical concepts to non-technical stakeholders. Good-to-Have Skills : - Experience with cloud platforms, particularly AWS. - Knowledge of MLOps practices and tools. - Experience with Natural Language Processing (NLP) techniques. - Exposure to Generative AI (GenAI) technologies.