GEN AI Engineer (Data Science)

Greetings from TATA CONSULTANCY SERVICES! Skill: Python Programming, Data Structures, and Software Design, NLP Fundamentals and Transformer-based Models, Deep Learning (Model Training, Fine-tuning, Optimization), Agentic AI Systems (Tool Use, Planning, Memory, Orchestration), Retrieval-Augmented Generation (RAG), Evaluation, and Deployment (MLOps) Years of Experience: 6-10 years Location: Chennai/Bangalore/Kolkata/Noida/Hyderabad/Kochi/Pune

:- • Profound insight into Python programming, data structures, and object-oriented / functional design principles • Strong expertise in Natural Language Processing (NLP), including text preprocessing, embeddings, classification, and information extraction • Solid understanding of Transformer-based architectures and Large Language Models (LLMs) • Hands-on experience with Deep Learning frameworks such as PyTorch or TensorFlow for model training and fine-tuning • Experience in building and fine-tuning NLP models using techniques such as transfer learning, • Strong understanding of Agentic AI systems, including tool invocation, planning, memory management, and workflow orchestration • Experience designing and implementing Retrieval-Augmented Generation (RAG) pipelines using vector databases and embedding models • Experience with model evaluation techniques, including offline metrics, qualitative human evaluation, and automated validation • Hands-on experience with performance optimization, cost control, and latency reduction in AI/ML pipelines • Familiarity with RESTful APIs and microservice-based architectures for deploying AI services • Familiarity with build, packaging, and version control tools such as Git, Docker, and CI/CD pipelines • Good understanding of containerization and deployment using Docker, Kubernetes, and cloud platforms (Azure/AWS/GCP)

: • Design, build, and maintain agentic AI systems using Python, LLMs, and modern NLP/deep learning techniques • Develop efficient, reusable, and reliable AI pipelines, including data ingestion, retrieval, reasoning, and generation layers • Ensure accuracy, robustness, safety, and responsiveness of AI-powered applications • Implement and manage tool-based workflows, enabling agents to interact with external systems and services • Design and optimize RAG-based solutions to improve grounding, reduce hallucinations, and enhance answer quality • Establish evaluation frameworks and monitoring to track model quality, drift, latency, and cost • Maintain high standards of code quality, documentation, testing, and automation • Collaborate with cross-functional teams to translate business