1047 Llm Jobs - Page 26

In this role, you will: Build Large Language Model (LLM) powered applications that support powerful capabilities for internal Wells Fargo users, with a focus on agentic workflows, prompt engineering, evaluations, model feedback and tuning mechanisms, and safety guardrails. Build systems utilizing Large Language Models (LLM) involving data ingestion pipelines, reliable service orchestration, backend application state management, and data layer implementation. Review and analyze complex, large-scale technology solutions for tactical and strategic business objectives and technical challenges that require in-depth evaluation of multiple factors, including intangibles or unprecedented technical factors. Provide engineering support for AI driven solutions that enhance and streamline work processes, ensuring reliable, secure, and scalable applications aligned with business priorities. Act as a thought leader in developing standards and enterprise best practices for engineering complex and large-scale technology solutions for technology engineering disciplines. Design, code, test, and document solutions that align with both product specific and enterprise-wide goals. Lead the development of frameworks and solutions that provide both vertical support to specific business-aligned products and horizontal capabilities that can scale across multiple platforms. Collaborate and consult with key technical experts, senior technology teams, and external industry groups to resolve complex technical issues and achieve goals. Lead projects, teams, and service as a peer mentor. Stay current on emerging technologies in AI to continue to the strategy discussion for future capability development. Required Qualifications: 4+ years of Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education 4+ years of engineering experience building and supporting large scale customer facing products using modern technologies. 2+ years building and delivering products using AI technologies. Desired Qualifications: Experience in backend application software development, with ability to quickly adapt to C# and Python code bases. Strong understanding of Retrieval-Augmented Generation (RAG), prompt engineering and agentic workflows. Deep knowledge of implementing guardrails and advanced techniques for query enrichment and re-writing Expertise in test or eval driven development including data and error analysis ensuring robust and scalable AI software. Experience architecting and implementing agentic frameworks for autonomous multi-step reasoning and planning Solid grasp of parsing, chunking, indexing and re-ranking of multiple file formats Experience with Generative AI Operations, and enterprise-scale AI adoption strategies. Familiarity with enterprise-scale software systems and their integration within large organizations. Experience in enterprise AI model lifecycle management, AI compliance, and risk mitigation strategies. Strong understanding of human centered AI design for workplace applications. Proven ability to work across multiple teams and functions to drive alignment and execution. Demonstrated success in designing usability and delivering seamless user experience. Excellent collaboration, communication, and problem-solving skills.

About this role: Wells Fargo is seeking a Lead Software Engineer to provide product support and drive the evolution of Artificial Intelligence (AI) enabled applications aligned to employee-facing lines of business in Enterprise Functions Technology (EFT). The EFT AI team will focus on building intelligent, efficient, and user-centric tools to accelerate the work of users in Finance, Human Resources, Risk, Audit, Legal, Public Affairs, Diverse Segments and EFT Platform Technology. In addition to hands-on development, this role will play a key part in evolving the operating model to provide both vertical (product specific) and horizontal (cross product/platform) support. Your work will directly align with Wells Fargos mission to innovate and enhance the employee experience using advanced technology. The successful candidate will be able to build and maintain reliable, secure, and scalable AI solutions that improve employee workflows and enable seamless system integrations. As a key technical contributor, you will collaborate with cross-functional teams to align solutions with the mission of transforming the Wells Fargo employee experience. In this role, you will: Build Large Language Model (LLM) powered applications that support powerful capabilities for internal Wells Fargo users, with a focus on agentic workflows, prompt engineering, evaluations, model feedback and tuning mechanisms, and safety guardrails. Build systems utilizing Large Language Models (LLM) involving data ingestion pipelines, reliable service orchestration, backend application state management, and data layer implementation. Review and analyze complex, large-scale technology solutions for tactical and strategic business objectives and technical challenges that require in-depth evaluation of multiple factors, including intangibles or unprecedented technical factors. Provide engineering support for AI driven solutions that enhance and streamline work processes, ensuring reliable, secure, and scalable applications aligned with business priorities. Act as a thought leader in developing standards and enterprise best practices for engineering complex and large-scale technology solutions for technology engineering disciplines. Design, code, test, and document solutions that align with both product specific and enterprise-wide goals. Lead the development of frameworks and solutions that provide both vertical support to specific business-aligned products and horizontal capabilities that can scale across multiple platforms. Collaborate and consult with key technical experts, senior technology teams, and external industry groups to resolve complex technical issues and achieve goals. Lead projects, teams, and service as a peer mentor. Stay current on emerging technologies in AI to continue to the strategy discussion for future capability development. Required Qualifications: 5+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education 5+ years of engineering experience building and supporting large scale customer facing products using modern technologies. 2+ years building and delivering products using AI technologies. Desired Qualifications: Experience in backend application software development, with ability to quickly adapt to C# and Python code bases. Strong understanding of Retrieval-Augmented Generation (RAG), prompt engineering and agentic workflows. Deep knowledge of implementing guardrails and advanced techniques for query enrichment and re-writing Expertise in test or eval driven development including data and error analysis ensuring robust and scalable AI software. Experience architecting and implementing agentic frameworks for autonomous multi-step reasoning and planning Solid grasp of parsing, chunking, indexing and re-ranking of multiple file formats Experience with Generative AI Operations, and enterprise-scale AI adoption strategies. Familiarity with enterprise-scale software systems and their integration within large organizations. Experience in enterprise AI model lifecycle management, AI compliance, and risk mitigation strategies. Strong understanding of human centered AI design for workplace applications. Proven ability to work across multiple teams and functions to drive alignment and execution. Demonstrated success in designing usability and delivering seamless user experience. Excellent collaboration, communication, and problem-solving skills. Job Expectations: Be Humble: You're smart yet always interested in learning from others Work Transparently: You always deal in an honest, direct, and transparent way. Take Ownership: You embrace responsibility and find joy in having the answers Learn More: You regularly self-educate and improve your skill set. Show Gratitude: You show appreciation and respect to those you work with.

Role & responsibilities Develop and deploy advanced AI/ML solutions, including RAG-based knowledge assistants and full-stack applications with MLFlow integration. Conduct extensive prompt tuning and evaluation for foundational LLMs to optimize model performance and output quality. Contribute to MLOps practices, including model benchmarking, deployment, and monitoring. Expertise in Python programming and strong proficiency in SQL and Spark. Extensive experience with MLOps frameworks like MLFlow and Docker. Proficiency in cloud platforms and services, including Databricks, Azure ML, and Vertex AI. Demonstrated experience with LLM concepts, Langchain, and agentic workflows. Excellent problem-solving, communication, and collaboration skills.

Dear candidate, We found your profile suitable for our current opening, please go through the below JD for better understanding of the role, Job Description : Role : Data Scientist / Senior Data scientist/ Lead Data scientist Exp : 6 - 12years Mode of work : Hybrid Model Work Location : Hyderabad/Bangalore/Noida/Pune/Kolkata Role Overview: We are seeking a highly skilled Generative AI Engineer with a strong foundation in data science and a passion for building cutting-edge AI solutions. The ideal candidate will have 6 - 10years of overall experience, including at least 2-3 years of hands-on work in Generative AI and Large Language Models (LLMs). You will play a key role in developing and deploying advanced AI models to drive innovation and impact across our platform. Responsibilities: Design and develop advanced generative models/large language models Collaborate with other teams to integrate AI solutions into our marketing platform Manage the project life cycle from research and development to deployment and optimization. Stay updated on and contribute to the latest advancements in AI research, applying new findings to ongoing projects. Ensure ethical AI development practices, prioritizing fairness, transparency, and privacy. Qualifications and Education: 2-3 years experience in GenAI and LLM with overall 6- 12 years of experience building advanced analytics products. Strong proficiency in Python and AI frameworks. Experience in conceptualizing, developing, and leading large-scale, end-to-end projects in Generative AI, MLOps, and LLMOps Working knowledge of generative models (GANs, VAEs) and NLP. Solid understanding of machine learning algorithms and data pre-processing techniques. Knowledge of cloud computing and AI deployment (Google Cloud preferred) Excellent problem-solving, analytical, and creative thinking skills Bachelors degree in computer science or engineering discipline Experience with MySQL and/or PostgreSQL databases. Strong inter-personal communication and collaboration skills. The candidate must be able to work independently & must possess strong communication skills. A good team player, high level of personal commitment & 'can do' attitude. Demonstrable ownership, commitment and willingness to learn new technologies and frameworks Please check below link for organisation details, https://www.tavant.com/ If interested , please drop your resume to dasari.gowri@tavant.com Regards Dasari Krishna Gowri Associate Manager - HR www.tavant.com

Role & responsibilities Required Skills: Strong Python programming experience, especially with pandas, numpy, matplotlib, seaborn. Experience in building monitoring dashboards or visualizations (e.g., Plotly, Dash, Streamlit). Understanding of ML model evaluation metrics (e.g., precision, recall, drift, AUC). Familiarity with model risk management concepts or frameworks. Ability to write clean, well-documented code for reproducibility and audit-readiness. Comfortable interpreting and working with structured model output and log files. Excellent attention to detail and communication skills. Should have Experience in Banking Domain Preferred candidate profile Notice Period: Immediate to 30 Days

Role: Prompt Engineer Location: Hyderabad / Indore / Bangalore Job Description: 1. Proven experience as a Prompt Engineer or in a related AI/NLP role. 2. Strong understanding of AI, machine learning, and NLP concepts and technologies. 3. Proficiency in programming languages relevant to AI (e.g., Python) and experience with AI tools such as ChatGPT or similar LLMs. 4. Excellent analytical, problem-solving, and communication skills. 5. Experience in data analysis, experimental design, and prompt optimization techniques (e.g., zero-shot, few-shot, chain-of-thought prompting). 6. Ability to collaborate effectively with cross-functional teams and manage multiple projects. 7. Attention to detail, creativity, and a passion for leveraging AI to solve real-world problems. 8. Relevant degree or certifications

Job Description AI/ML Engineer We are seeking a sharp, innovative, and passionate AI/ML Engineer to join our R&D team. You will be responsible for designing and deploying intelligent algorithms that enhance the predictive capabilities of our remote diagnostics platform used in railway signaling and other critical infrastructure. Responsibilities: Build and deploy machine learning and deep learning models for predictive maintenance and anomaly detection. Analyze time-series sensor data to develop accurate failure prediction models. Work with massive datasets – cleaning, preprocessing, feature engineering, and model tuning. Develop and expose ML models as REST APIs for integration into cloud-based systems. Collaborate with domain experts, data engineers, and full stack developers to create robust, production-grade solutions. Perform continuous evaluation and improvement of deployed models (MLOps). Stay updated with the latest AI research and evaluate them for real-world application. Required Skills & Qualifications: 2–5 years of experience in AI/ML project development and deployment. Proficiency in Python , Scikit-learn , Pandas , NumPy . Hands-on experience with TensorFlow or PyTorch . Solid understanding of ML algorithms , classification , regression , clustering , NLP , or computer vision . Experience with model deployment , APIs , and Docker . Knowledge of cloud platforms (AWS/GCP/Azure) is a plus. Strong problem-solving skills and understanding of statistical concepts. Nice to Have: Experience with time-series forecasting and anomaly detection. Prior exposure to predictive maintenance in industrial environments. Familiarity with IoT data and sensor networks.

Role & responsibilities IT developer OJT(On The JOB Training) with AI, Web, Mobile, AI, Android, Flutter, PHP, REACT-NODE, python-django & ML/DL Internships are available For NON IT graduates, we will train you in live AI/IOT/Web/Mobile projects of USA startups. Full time opportunity after Successful Internships. Additional training will be provided for freshers along with 2 year Master degree & 3 year degree enrollments. Preferred candidate profile Fast & SLOW self learner programs. Good understanding of Accounting, healthcare and business application exposure is required.

About the Role A key enabler of our services is leveraging data in delivering client solutions. As customer datasets grow richer and business challenges become more complex, ZS's Advanced Data Science function plays a central role in developing and delivering innovative solutions. This team focuses on: Researching evolving datasets and advanced analytical techniques to develop new offerings/solutions Delivering measurable client impact by implementing these solutions Providing thought leadership by shaping ZS's point of view in the data science space What You'll Do Lead and mentor a team of data scientists, ensuring delivery excellence across client engagements Design and implement advanced data science strategies that align with organizational goals Apply statistical, algorithmic, and visualization techniques to model complex business problems and uncover insights Drive business and strategy development through project delivery and client relationship management Develop and sell new business opportunities through data science-based solutions Oversee multiple projects, managing timelines, deliverables, and team performance Play a key role in developing scalable data science capabilities and reusable analytic platforms Motivate and coach team members; serve as a role model in fostering collaboration and continuous learning Contribute to thought leadership through research, publication, and innovation initiatives What You'll Bring PhD in Computer Science, Statistics, or a related field preferred (10+ years experience required in lieu of PhD) 8+ years of relevant work experience in data science, analytics, or AI Strong expertise in advanced analytics techniques such as predictive modeling, NLP, Gen AI, recommender systems, and social listening Hands-on experience with big data technologies and platforms (e.g., AWS, Azure) Proficiency in Python or R for statistical programming Experience managing agile projects and data science delivery across cross-functional teams Strong business acumen and a proven track record of developing client relationships and business development Previous experience in healthcare, pharma, or life sciences domains preferred Excellent communication skillsboth verbal and writtenwith the ability to present complex ideas to senior stakeholders A detail-oriented, research-driven mindset with strong critical thinking and problem-solving capabilities

Expertise in Python Experience developing RESTful API services leveraging libraries such as FastAPI Experience with PostgreSQL Experience with developing applications that use LLMs Experience with Docker containerization Docker

Job Title: Generative AI Engineer Location: Bangalore or Hyderabad Experience Required: 35 years (with 12 years in Generative AI/LLMs) Employment Type: Full-Time About the Role We are looking for a passionate and experienced Generative AI Engineer to join our AI/ML team. In this role, you will work with technologies like OpenAI, LLaMA, and Databricks-hosted models to design, build, and deploy real-world GenAI solutions. You’ll be involved in everything from feeding data into models, improving their performance, to integrating them into live products. Key Responsibilities Develop and deploy LLM-based solutions using GPT, LLaMA, or fine-tuned models Manage end-to-end GenAI projects from proof of concept to production Build scalable data pipelines for LLM training and inference Optimize model performance using prompt engineering, fine-tuning, DeepSpeed, and other techniques Host and manage models on Databricks and integrate them with data platforms Collaborate with data scientists, MLOps, and product teams to ensure AI solutions meet business goals Continuously enhance the performance, scalability, and usability of LLM-based systems Required Skills 3–5 years of engineering experience, with 1–2 years in Generative AI or LLMs Strong Python skills and experience with AI/ML frameworks Hands-on experience with OpenAI APIs, LLaMA models, LangChain, DeepSpeed Experience working with Databricks (Unity Catalog, MLflow) and AWS services (S3, EC2, Lambda) Good understanding of LLM architecture and model optimization techniques Proven track record in deploying real-world GenAI applications

Job Title: Principal Machine Learning Architect Location: Bengaluru, Hybrid Department: Engineering / Data Science Reports To: Head of AI/ML About the Role: We are seeking a highly experienced Principal Machine Learning Architect to lead the design, development, and deployment of large-scale, high-impact ML solutions across our organization. As a thought leader, you will drive the ML roadmap, architect end-to-end solutions, and ensure our ML infrastructure is scalable, secure, and production-ready. Key Responsibilities: Architecture Leadership: Design and oversee the implementation of scalable, reliable, and efficient machine learning architectures. End-to-End Ownership: Lead the entire ML lifecycle from problem definition, data ingestion, feature engineering, model development, deployment, and monitoring. Strategic Guidance: Define best practices for ML system design, governance, LLM, NLP, Deep Learning, Machine Learning, RAG, MLOps, and responsible AI practices. Collaboration: Work cross-functionally with data scientists, ML engineers, product managers, and infrastructure teams to ensure alignment and integration. Innovation: Evaluate and incorporate new tools, techniques, and research to improve model performance and infrastructure. Mentorship: Provide technical mentorship to ML engineers and data scientists; promote a culture of technical excellence and continuous learning. Scalability: Architect and optimize ML solutions for scalability, performance, and maintainability in cloud and hybrid environments. Security & Compliance: Ensure models and data pipelines adhere to security, fairness, and compliance standards. Required Qualifications: 10+ years of experience in software engineering or data science with at least 5 years in ML architecture or applied ML roles. Advanced degree (MS/PhD) in Computer Science, Machine Learning, Data Science, or a related field. Proven experience building and deploying ML systems at scale (e.g., recommendation systems, NLP, computer vision, time series). Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). Deep understanding of data infrastructure, distributed computing, and modern MLOps practices (e.g., MLflow, Kubeflow, Airflow). Familiarity with cloud platforms (AWS, GCP, Azure) and container technologies (Docker, Kubernetes). Strong knowledge of data privacy, model governance, and responsible AI principles. Preferred Qualifications: Experience with generative AI or LLM deployment. Contributions to open-source ML projects or research publications. Familiarity with data mesh, feature stores, or real-time ML pipelines. Strong communication and leadership skills.Role & responsibilities Preferred candidate profile

Key Responsibilities Design and deploy Large Language Model (LLM)-based applications and GenAI systems. Work closely with internal teams and client stakeholders to gather requirements and deliver scalable AI solutions. Develop and integrate Retrieval-Augmented Generation (RAG) pipelines. Build, test, and deploy APIs and services that bring AI capabilities into business platforms. Perform model optimization, evaluation, and prompt engineering. Mentor junior team members and review technical deliverables. Ensure security, compliance, and ethical usage of AI models in regulated domains. Collaborate with product, design, and engineering teams to shape AI solutions for enterprise clients. Technical Requirements Must-Have Skills 7-10 years of overall experience in AI/ML, with 2-3 years in Generative AI and LLMs. Strong coding skills in Python; familiarity with PyTorch, TensorFlow, or JAX. Experience with: HuggingFace Transformers, LangChain, and OpenAI/Cohere/Claude APIs. Prompt engineering, fine-tuning, and custom embeddings. Proficiency in developing and deploying APIs using FastAPI or Flask. Experience with Docker and containerized deployment. Cloud exposure: Azure, AWS, or GCP, especially ML services like SageMaker, Vertex AI, or Azure ML. Nice-to-Have Skills Experience with vector databases (FAISS, Pinecone, Weaviate). Knowledge of MLOps pipelines and CI/CD for ML workflows. Understanding of responsible AI practices, hallucination reduction, and bias detection. Previous work experience in finance, audit, regulatory, or consulting sectors. Additional Information Leadership in AI project delivery and solution design. Excellent communication and stakeholder engagement skills. Team player with mentoring abilities and technical decision-making confidence. Adaptability to dynamic project requirements and regulatory constraints. Excellent communication and interpersonal skills. Strong problem-solving and debugging skills. Proactive in delivering quality work within deadlines.

Job description: Data Scientist Specialist (NLP, LLM, and GenAI) Roles and responsibilities: • Collaborate with Product, Data, and Technology teams to design and create a suite of new microservices powered by AI • Work alongside a team of data scientists in applying state-of-the-art LLMs from OpenAI, Anthropic, Meta, and Mistral • Create NLP pipelines for Named Entity Recognition and Entity Resolution • Assess the quality and robustness of your solutions and iterate to optimize performance • Apply best practices of MLOps / LLMOps in deploying new products and services to production We expect you to have: • 7+ years processing structured and unstructured data using NLP • 1+ years applying LLMs and RAG to solve real-world, industry problems • Solid understanding of NLP and related concepts such as vectorization, entity resolution, string similarity, and semantic search • Python experience is required, AWS/ FastAPI / Gradio / Docker and LLM fine-tuning is a plus • Cloud and systems experience in AWS S3/EC2, Databricks, and linux • Solid understanding of Git or other version control software • Passionate about Generative AI, consistently tracking the latest industry developments • Strong organizational and communication skills, both verbal and written • Ambitious and enthusiastic about working in a fast-paced, startup style environment • Bachelor's degree in computer science, Computer Engineering, or related discipline. Masters preferred. It would be phenomenal if you: • Have previous experience crafting financial data products. • Have strong aptitude and/or interest in financial markets

Product Knowledge, E-Commerce , taxonomy, ontology and catalog data to enhance search, recommendations, and AI-powered experiences. High-agency, AI/ML and product analytics. Writing SQL queries, automating workflows in Python and LLMs.

About the Role We are seeking a talented AI Engineer with 2-3 years of professional experience to join our growing team. As a AI Engineer, you will collaborate with data scientists, software engineers, and product teams to design, build, and deploy ML models that drive value across our organisation. This is an excellent opportunity for someone early in their ML career who wants to gain hands-on experience with real-world applications while working alongside experienced professionals. Responsibilities Implement and optimize ML algorithms and models based on requirements from data scientists and product teams Work with Large Language Models (LLMs) to develop AI-powered applications and features Design prompts, fine-tune, and evaluate LLM performance for specific use cases Build systems to integrate LLMs with existing products and services Design and develop ML/DL pipelines for data preprocessing, model training, evaluation, and deployment Experience in using RAG Collaborate with software engineers to integrate ML/DL models into production systems Monitor and maintain deployed ML/DL models, ensuring reliability and performance Participate in code reviews and contribute to engineering best practices Stay current with the latest ML/DL research and technologies, evaluating their potential application to business problems Assist in collecting and preparing datasets for model training and validation Document model architecture, data flows, and deployment procedures Strong in Machine Learning and Deep learning Basics Requirements Bachelor's degree in Computer Science, Statistics, Applied Mathematics, or a related field (Master's preferred but not required) 1-2 years of professional experience building and deploying machine learning models Strong programming skills in Python and familiarity with ML frameworks such as TensorFlow, PyTorch, or scikit-learn Experience with Large Language Models (LLMs), including understanding of transformer architectures, fine-tuning approaches, and prompt engineering Knowledge of NLP concepts and techniques applicable to LLMs (embeddings, tokens, context windows, etc.) Experience with data processing libraries like Pandas, NumPy, and visualization tools like Matplotlib or Seaborn Understanding of ML fundamentals, including supervised/unsupervised learning, feature engineering, and model evaluation Knowledge of software engineering best practices (version control, testing, CI/CD) Basic understanding of cloud platforms (AWS, GCP, or Azure) and containerization (Docker)

Role & responsibilities Azure DevOps: 3+ years building and managing pipelines, release gates, and service connections. • Python Ecosystem: Strong scripting for custom pipeline tasks; familiarity with FastAPI, Celery & Redis for async workflows. • Gen AI / LangChain: Working knowledge of orchestrating large language models (LLMs) in production. • Infrastructure as Code: Hands-on with Terraform or ARM/Bicep to provision Azure resources. • Containerization & Orchestration: Docker, Kubernetes (AKS), Helm charts. • Databases: Deployment and management of PostgreSQL in Azure (Azure Database for PostgreSQL). • Frontend Delivery: Familiarity with building, packaging, and deploying Angular applications (including responsive UI considerations). • Identity & Access: Azure Entra ID (Azure AD) integration for service-to-service and user authentication. • Monitoring & Logging: Define metrics and alerts in Azure Monitor / Application Insights. ________________________________________ Nice-to-Have • Experience with SQLAlchemy for database migrations and ORM in pipelines. • Familiarity with LangChains callback handlers and custom agent deployments. • Knowledge of advanced Celery features (chords, retry policies, rate limiting). • Exposure to client-side performance monitoring (Lighthouse, Webpack optimizations). • Scripting with PowerShell or Azure CLI for ad-hoc automation. • Azure DevOps Engineer Expert (AZ-400) or equivalent hands-on certification. ________________________________________ Personal Attributes • Strong communicator who can bridge DevOps, development, and data science teams. • Proactive problem-solver, comfortable in high-velocity Agile environments. • Attention to detail " Mandatory Skills "Required Skills & Experience • Azure DevOps: 3+ years building and managing pipelines, release gates, and service connections. • Python Ecosystem: Strong scripting for custom pipeline tasks; familiarity with FastAPI, Celery & Redis for async workflows. • Gen AI / LangChain: Working knowledge of orchestrating large language models (LLMs) in production. • Infrastructure as Code: Hands-on with Terraform or ARM/Bicep to provision Azure resources. • Containerization & Orchestration: Docker, Kubernetes (AKS), Helm charts. • Databases: Deployment and management of PostgreSQL in Azure (Azure Database for PostgreSQL). • Frontend Delivery: Familiarity with building, packaging, and deploying Angular applications (including responsive UI considerations). • Identity & Access: Azure Entra ID (Azure AD) integration for service-to-service and user authentication. • Monitoring & Logging: Define metrics and alerts in Azure Monitor / Application Insights." Nice to have skills "Nice-to-Have • Experience with SQLAlchemy for database migrations and ORM in pipelines. • Familiarity with LangChain’s callback handlers and custom agent deployments. • Knowledge of advanced Celery features (chords, retry policies, rate limiting). • Exposure to client-side performance monitoring (Lighthouse, Webpack optimizations). • Scripting with PowerShell or Azure CLI for ad-hoc automation. • Azure DevOps Engineer Expert (AZ-400) or equivalent hands-on certification. Preferred candidate profile

Description - External Optum is a global organization that delivers care, aided by technology to help millions of people live healthier lives. The work you do with our team will directly improve health outcomes by connecting people with the care, pharmacy benefits, data and resources they need to feel their best. Here, you will find a culture guided by diversity and inclusion, talented peers, comprehensive benefits and career development opportunities. Come make an impact on the communities we serve as you help us advance health equity on a global scale. Join us to start Caring. Connecting. Growing together. Primary Responsibilities: Lead and manage a team of data scientists, fostering a collaborative and innovative environment Oversee the design and development of predictive models and machine learning algorithms Collaborate with business stakeholders to understand and define their data needs Translate business problems into data-driven solutions, presenting complex results in a clear and actionable manner Monitor model performance and continuously improve the accuracy of predictions and insights Maintain up-to-date knowledge of the latest data science trends and technologies, implementing new methodologies as appropriate Promote data literacy throughout the organization, educating staff on how to use data to drive decision-making Ensure data integrity and compliance with privacy regulations Comply with the terms and conditions of the employment contract, company policies and procedures, and any and all directives (such as, but not limited to, transfer and/or re-assignment to different work locations, change in teams and/or work shifts, policies in regards to flexibility of work benefits and/or work environment, alternative work arrangements, and other decisions that may arise due to the changing business environment). The Company may adopt, vary or rescind these policies and directives in its absolute discretion and without any limitation (implied or otherwise) on its ability to do so Role & responsibilities Required Qualifications: Bachelor of Engineering or equivalent 10+ years of total experience Coding : Python, pandas, numpy Math behind ML and DL algorithms Basics of Statistics ML and DL concepts Preferred Qualification: Exposure GenAI and LLMs Preferred candidate profile Perks and benefits

Role & Responsibility Develop and maintain microservice architecture and API management solutions using REST and gRPC for seamless deployment of AI solutions. • Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization. • Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models. • Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements. • Familiarity with tools like Terraform, CloudFormation, and Pulumi for efficient infrastructure management. • Create and manage CI/CD pipelines using Git-based platforms (e.g., GitHub Actions, Jenkins) to ensure streamlined development workflows. • Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments. • Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment. • Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development. • Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC. • Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs. • Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling. • Design and execute rigorous A/B tests for machine learning models, analyzing results to drive strategic improvements and decisions. • Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function. • Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment. Technical Skills: • Advanced proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow) • Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques • Experience with big data processing using Spark for large-scale data analytics • Version control and experiment tracking using Git and MLflow • Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing. • DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations. • LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management. • MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining. • Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems. • LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security. • General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP. • Experience in creating LLD for the provided architecture. • Experience working in microservices based architecture.

Roles and Responsibilities Develop and maintain Microservice architecture and API management solutions using REST and gRPC for seamless deployment of AI solutions. Collaborate with cross-functional teams, including data scientists and product managers, to acquire, process, and manage data for AI/ML model integration and optimization. Design and implement robust, scalable, and enterprise-grade data pipelines to support state-of-the-art AI/ML models. Debug, optimize, and enhance machine learning models, ensuring quality assurance and performance improvements. Familiarity with tools like Terraform, CloudFormation, and Pulumi for efficient infrastructure management. Create and manage CI/CD pipelines using Git-based platforms (e.g., GitHub Actions, Jenkins) to ensure streamlined development workflows. Operate container orchestration platforms like Kubernetes, with advanced configurations and service mesh implementations, for scalable ML workload deployments. Design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment. Engage in advanced prompt engineering and fine-tuning of large language models (LLMs), focusing on semantic retrieval and chatbot development. Document model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC. Research and implement cutting-edge LLM optimization techniques, such as quantization and knowledge distillation, ensuring efficient model performance and reduced computational costs. Collaborate closely with stakeholders to develop innovative and effective natural language processing solutions, specializing in text classification, sentiment analysis, and topic modeling. Design and execute rigorous A/B tests for machine learning models, analyzing results to drive strategic improvements and decisions. Stay up-to-date with industry trends and advancements in AI technologies, integrating new methodologies and frameworks to continually enhance the AI engineering function. Contribute to creating specialized AI solutions in healthcare, leveraging domain-specific knowledge for task adaptation and deployment. Technical Skills: Advanced proficiency in Python . Extensive experience with LLM frameworks (Hugging Face Transformers, LangChain) and prompt engineering techniques Experience with big data processing using Spark for large-scale data analytics Version control and experiment tracking using Git and MLflow Software Engineering & Development: Advanced proficiency in Python, familiarity with Go or Rust, expertise in microservices, test-driven development, and concurrency processing. DevOps & Infrastructure: Experience with Infrastructure as Code (Terraform, CloudFormation), CI/CD pipelines (GitHub Actions, Jenkins), and container orchestration (Kubernetes) with Helm and service mesh implementations. LLM Infrastructure & Deployment: Proficiency in LLM serving platforms such as vLLM and FastAPI, model quantization techniques, and vector database management. MLOps & Deployment: Utilization of containerization strategies for ML workloads, experience with model serving tools like TorchServe or TF Serving, and automated model retraining. Cloud & Infrastructure: Strong grasp of advanced cloud services (AWS, GCP, Azure) and network security for ML systems. LLM Project Experience: Expertise in developing chatbots, recommendation systems, translation services, and optimizing LLMs for performance and security. General Skills: Python, SQL, knowledge of machine learning frameworks (Hugging Face, TensorFlow, PyTorch), and experience with cloud platforms like AWS or GCP. Experience in creating LLD for the provided architecture. Experience working in microservices based architecture. Domain Expertise: Deep understanding of ML and LLM development lifecycle, including fine-tuning and evaluation Expertise in feature engineering, embedding optimization, and dimensionality reduction Advanced knowledge of A/B testing, experimental design, and statistical hypothesis testing Experience with RAG systems, vector databases, and semantic search implementation Proficiency in LLM optimization techniques including quantization and knowledge distillation Understanding of MLOps practices for model deployment and monitoring Professional Competencies: Strong analytical thinking with ability to solve complex ML challenges Excellent communication skills for presenting technical findings to diverse audiences Experience translating business requirements into data science solutions Project management skills for coordinating ML experiments and deployments Strong collaboration abilities for working with cross-functional teams Dedication to staying current with latest ML research and best practices Ability to mentor and share knowledge with team members

About the role PubMatic is looking for engineers with expertise in Generative AI and AI agent development. You will be responsible for building and optimizing advanced AI agents that leverage the latest technologies in Retrieval-Augmented Generation (RAG), vector databases, and large language models (LLMs). You will work on developing state-of-the-art solutions that enhance Generative AI capabilities and enable our platform to handle complex information retrieval, contextual generation, and adaptive interactions. What You'll do Collaborate with engineers, architects, product managers, and UX designers to develop innovative AI solutions for new customer use-cases. Work independently and iterate quickly based on customer feedback to make product tweaks. Implement and optimize LLMs for specific use cases, including fine-tuning models, deploying pre-trained models, and evaluating their performance. Develop AI agents powered by RAG systems, integrating external knowledge sources to improve the accuracy and relevance of generated content. Design, implement, and optimize vector databases (e.g., FAISS, Pinecone, Weaviate) for efficient and scalable vector search, and work on various vector indexing algorithms. Create sophisticated prompts and fine-tune them to improve the performance of LLMs in generating precise and contextually relevant responses. Utilize evaluation frameworks and metrics (e.g., Evals) to assess and improve the performance of generative models and AI systems. Work with data scientists, engineers, and product teams to integrate AI-driven capabilities into customer-facing products and internal tools. Stay up to date with the latest research and trends in LLMs, RAG, and generative AI technologies to drive innovation in the companys offerings. Continuously monitor and optimize models to improve their performance, scalability, and cost efficiency We'd Love for You to Have Must Have Strong understanding of large language models (GPT, BERT, T5, etc.) and their underlying principles, including transformer architecture and attention mechanisms. Proven experience building AI agents with Retrieval-Augmented Generation to enhance model performance using external data sources (documents, databases). In-depth knowledge of vector databases, vector indexing algorithms, and experience with technologies like FAISS, Pinecone, Weaviate, or Milvus. Ability to craft complex prompts to guide the output of LLMs for specific use cases, enhancing model understanding and contextuality. Familiarity with Evals and other performance evaluation tools for measuring model quality, relevance, and efficiency. Proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, and Hugging Face Transformers. Experience with data preprocessing, vectorization, and handling large-scale datasets. Ability to present complex technical ideas and results to both technical and non-technical stakeholders. Nice-to-Have Experience in building AI agents using graph-based architectures, including knowledge graph embeddings and graph neural networks (GNNs). Experience with training small base models using custom data, including data collection, pre-processing, and fine-tuning models to specific domains or tasks. Familiarity with deploying AI models on cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). Publication or contributions to research in AI, LLMs, or related fields. Qualifications Should have a bachelors degree in engineering (CS / IT) or equivalent degree from a well-known Institutes / Universities. If interested, please visit the link below to apply: https://pubmatic.com/job/?gh_jid=4615220008

About the role PubMatic is looking for engineers with expertise in Generative AI and AI agent development. You will be responsible for building and optimizing advanced AI agents that leverage the latest technologies in Retrieval-Augmented Generation (RAG), vector databases, and large language models (LLMs). You will work on developing state-of-the-art solutions that enhance Generative AI capabilities and enable our platform to handle complex information retrieval, contextual generation, and adaptive interactions. What You'll Do Provide technical leadership and mentorship to engineering teams while collaborating with architects, product managers, and UX designers to create innovative AI solutions that address complex customer challenges. Lead the design, development, and deployment of AI-driven features. Drive end-to-end ownershipfrom feasibility analysis and design specifications to execution and releasewhile ensuring quick iterations based on customer feedback in a fast-paced Agile environment. Spearhead technical design meetings and produce detailed design documents that outline scalable, secure, and robust AI architectures. Ensure that the solutions are aligned with long-term product strategy and technical roadmaps. Implement and optimize LLMs for specific use cases, including fine-tuning models, deploying pre-trained models, and evaluating their performance. Develop AI agents powered by RAG systems, integrating external knowledge sources to improve the accuracy and relevance of generated content. Design, implement, and optimize vector databases (e.g., FAISS, Pinecone, Weaviate) for efficient and scalable vector search, and work on various vector indexing algorithms. Create sophisticated prompts and fine-tune them to improve the performance of LLMs in generating precise and contextually relevant responses. Utilize evaluation frameworks and metrics (e.g., Evals) to assess and improve the performance of generative models and AI systems. Work with data scientists, engineers, and product teams to integrate AI-driven capabilities into customer-facing products and internal tools. Stay up to date with the latest research and trends in LLMs, RAG, and generative AI technologies to drive innovation in the companys offerings. Continuously monitor and optimize models to improve their performance, scalability, and cost efficiency We'd Love for You to Have Must Have Strong understanding of large language models (GPT, BERT, T5, etc.) and their underlying principles, including transformer architecture and attention mechanisms. Proven experience building AI agents with Retrieval-Augmented Generation to enhance model performance using external data sources (documents, databases). In-depth knowledge of vector databases, vector indexing algorithms, and experience with technologies like FAISS, Pinecone, Weaviate, or Milvus. Ability to craft complex prompts to guide the output of LLMs for specific use cases, enhancing model understanding and contextuality. Familiarity with Evals and other performance evaluation tools for measuring model quality, relevance, and efficiency. Proficiency in Python and experience with machine learning libraries such as TensorFlow, PyTorch, and Hugging Face Transformers. Experience with data preprocessing, vectorization, and handling large-scale datasets. Ability to present complex technical ideas and results to both technical and non-technical stakeholders. Nice-to-Have Experience in building AI agents using graph-based architectures, including knowledge graph embeddings and graph neural networks (GNNs). Experience with training small base models using custom data, including data collection, pre-processing, and fine-tuning models to specific domains or tasks. Familiarity with deploying AI models on cloud platforms (AWS, GCP, Azure) and containerization technologies (Docker, Kubernetes). Publication or contributions to research in AI, LLMs, or related fields. Qualifications Should have a bachelors degree in engineering (CS / IT) or equivalent degree from a well-known Institutes / Universities. If interested, please visit the link below to apply: https://pubmatic.com/job/?gh_jid=4615235008

Key Responsibilities: Write efficient, readable, reusable, and maintainable APEX code. Ensure adherence to Salesforce security and data integrity best practices. Participate in code reviews to ensure compliance with code quality standards. Manage integrations with external APIs and third-party applications. Interact with LLM systems by asking questions based on open-source datasets, verifying outputs, and providing feedback. Required Skills: 4+ years of Salesforce development experience, with a strong focus on APEX, Lightning Components (Aura), LWC, and VF pages. Salesforce certifications: PD1 (Platform Developer I) required, PD2 (Platform Developer II) is an asset. Strong understanding of Salesforce APIs, integration patterns, and best practices. Excellent spoken and written English communication skills. Proficiency in tackling algorithmic problems. Bachelor's/Masters degree in Engineering, Computer Science, or equivalent experience. Salesforce development with a strong focus on APEX, Lightning Components (Aura), LWC, and VF pages. Strong understanding of Salesforce APIs and integration patterns. Proficiency in tackling algorithmic problems.

Who we look for You are a proven full stack engineer. Not only strong technically, you have shown that you can work effectively with product managers, designers and other engineering teams. You have a fierce sense of ownership, caring deeply about the quality of everything that you deliver into your clients hands. You love the challenge of engineering and are confident in your ability to bring clarity and direction to ambiguous problem spaces. You work well in a fast-paced environment while deeply invest in long term quality and efficiency. Basic Qualifications 1 to 3 years of hands-on development experience in Core Java (Java 11-21) or Python, and proficiency in backend technologies such as Databases (SQL/No-SQL), Elastic Search, MongoDB, Spring framework, REST, GraphQL, Hibernate, etc. Experience with front-end development with React, Redux, Vue, Typescript, and/or similar frameworks. Demonstrated experience operating in a fast-paced Agile/Scrum setup with a global/remote team. Knowledge of developing and deploying applications in public cloud (AWS, GCP or Azure) or K8S. Experience with implementing unit tests, integration tests, Test Driven Development. Strong development, analytical and problem-solving skills. Knowledge of prompt engineering, LLM, AI Agents, etc. Preferred Qualifications Excellent communication skills and experience working directly with business stakeholders. Data modeling, warehousing (Snowflake, AWS Glue/ EMR, Apache Spark ) and a strong understanding of data engineering practices. Technical, Team or Project leadership experience. Some experience using Infrastructure-As-Code tools (e.g. AWS CDK, Terraform, CloudFormation) Experience with reactive, event-based architectures.

JOB DESCRIPTION Title: Senior Software Engineer - Data Science Full time Chennai Data Scientist Role Type: Location: Reports to: Summary of the profile: We are seeking a highly motivated and innovative Data Scientist with 3-5 years of experience to join our team, with a focus on leveraging Generative AI and Large Language Models (LLMs). The ideal candidate will be passionate about exploring the potential of these cutting-edge technologies to solve complex problems and create impactful solutions. You will collaborate with cross-functional teams to research, develop, and deploy generative AI models and LLM-powered applications. What youll do here: Explore and experiment with various generative AI models (e.g., GANs, VAEs, diffusion models) and LLMs (e.g., GPT, BERT, Llama). ¢ Fine-tune pre-trained LLMs for specific tasks and domains. Develop and implement generative models for tasks like data augmentation, content generation, and synthetic data creation. ¢ ¢ ¢ Prompt engineering, and prompt iteration. Collect, clean, and preprocess large text datasets for LLM training and evaluation. Perform natural language processing (NLP) tasks such as text classification, sentiment analysis, and entity recognition. ¢ ¢ Use LLMs to assist in data cleaning and data understanding. Develop and evaluate machine learning models, including those leveraging generative AI and LLMs. ¢ Evaluate model performance using relevant metrics and techniques, including those specific to LLMs (e.g., perplexity, BLEU score). ¢ ¢ Tune hyperparameters to optimize model performance. Create compelling visualizations and reports to communicate insights from generative AI and LLM experiments. ¢ ¢ Present findings to both technical and non-technical audiences. Collaborate with engineers, product managers, and researchers to define project requirements and deliver solutions. ¢ ¢ Communicate effectively with team members and stakeholders, both verbally and in writing. Stay up-to-date with the latest advancements in generative AI and LLMs. Copyright © RESULTICKS Solution Inc 1 ¢ ¢ Assist in deploying generative AI models and LLM-powered applications to production environments. Monitor model performance and retrain models as needed. What you will need to thrive: ¢ ¢ 3-5 years of professional experience in data science, with a growing interest in Generative AI and LLMs. Proficiency in Python and relevant libraries (e.g., NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Hugging Face Transformers). ¢ ¢ ¢ ¢ ¢ ¢ Experience with NLP techniques and libraries. Understanding of machine learning algorithms and statistical concepts. Experience with data visualization tools (e.g., Matplotlib, Seaborn, Tableau, Power BI). Excellent problem-solving and analytical skills. Strong communication and collaboration skills. Experience with Prompt engineering, version control systems like Git and cloud platforms like GCP, AWS or Azure is a plus Preferred Skills: ¢ ¢ ¢ ¢ ¢ ¢ Experience with fine-tuning LLMs for specific tasks. Experience with deploying LLMs in production environments. Knowledge of deep learning architectures relevant to generative AI and LLMs. Familiarity with vector databases. Experience with RAG (Retrieval Augmented Generation) architectures. Experience with the Langchain Library. Education: ¢ Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related field. Core competencies: ¢ Communication, Analytical & Problem-Solving Skills Copyright © RESULTICKS Solution Inc 2 About RESULTICKS RESULTICKS is a fully integrated connected experience platform for Real Time Audience Engagement. RESULTICKS empowers brands to create data-driven communications to their audiences and drives business decisions through its award-winning, AI-powered big data cloud solution. Supported by the worlds first customer data blockchain, RESULTICKS unlocks incredible digital transformation possibilities by delivering the full 360 view of the customer through data consolidation and omnichannel orchestration. Built from the ground up by experts in marketing, technology, and business management, RESULTICKS enables brands to make a transformational leap to conversion- driven, growth-focused personalized engagement. With an advanced CDP at its core. RESULTICKS offers AI-powered omnichannel orchestration, complete analytics, next-best engagement, attribution at the segment-of-one level, and the worlds first marketing block chain. ¢ RESULTICKS has been named to the Gartners Magic Quadrant 2021 for Multichannel Marketing Hubs for five years in a row and has been awarded with the Microsoft Elevating Customer Experience with AI award. ¢ Headquartered in Singapore and New York City, RESULTICKS is one of the top global martech solutions servicing both B2B/B2B2C and B2C segments. RESULTICKS global presence includes the United States, India, Singapore, Indonesia, Malaysia, Vietnam, Thailand, and the Philippines. Watch video on RESULTICKS - https://www.youtube.com/watch?v=G_OwGy6unP8 Copyright © RESULTICKS Solution Inc 3