AI Agentic Engineer

Must Have Technical / Functional Skills Core Engineering & Programming: Strong software engineering fundamentals with expert‑level proficiency in Python. Experience with Java, Go, or TypeScript is a strong plus. LLM & GenAI Application Development: Proven, hands‑on experience building and deploying production‑grade applications using Large Language Models (LLMs) like GPT, Claude, or Gemini. This must go beyond simple API calls and include experience with tool/function‑calling, structured outputs, and evaluation. Agentic AI Frameworks & Orchestration: Demonstrable expertise in designing and implementing agentic workflows using frameworks like LangGraph, Semantic Kernel, AutoGen, or similar. Experience with multi‑agent systems, planning, and autonomous execution is critical. RAG (Retrieval‑Augmented Generation): Deep, practical knowledge of building and optimizing RAG pipelines. This includes data ingestion, various chunking strategies, embeddings, vector databases (e.g., Pinecone, Chroma, FAISS), and hybrid search/reranking. Production & MLOps: Experience with production engineering practices, including building scalable APIs (REST, RPC), microservices, CI/CD pipelines, containerization (Docker, Kubernetes), and cloud platforms (AWS, Azure, or GCP). Roles & Responsibilities Agentic System Design & Engineering: Architect, build, and deploy advanced AI agents capable of autonomous reasoning, decision‑making, and self‑directed task execution. Design and implement complex, multi‑step agentic workflows that integrate with enterprise APIs, data sources, and platforms. RAG and Grounding Implementation: Develop robust RAG pipelines to ground agent responses in factual, reliable data. This includes managing the full lifecycle from data ingestion and vectorization to retrieval and citation. Tooling and Integration: Build and maintain the "tools" that agents use to interact with the digital world. Create secure, well‑documented tool interfaces for internal services, databases, and third‑party APIs. Evaluation, Guardrails & Safety: Design and implement comprehensive evaluation frameworks to measure agent performance, accuracy, and reliability. Develop and enforce safety guardrails, policy checks, and fallback mechanisms to ensure agents operate safely and predictably in production environments. Optimization and Productionization: Debug, monitor, and optimize agentic systems for latency, cost, and efficiency. Own the end‑to‑end deployment process, including CI/CD, structured logging, and incident response for AI systems. Generic Managerial Skills Problem‑Solving & Critical Thinking: Ability to analyze complex, ambiguous problems and design innovative, practical solutions. Thrives in navigating the uncertainty inherent in emerging AI technologies. Collaboration & Communication: Excellent communication skills with the ability to articulate complex technical concepts to both technical and non‑technical stakeholders. Proven experience working cross‑functionally with product, research, and infrastructure teams. Ownership & Leadership: A bias for action and a strong sense of ownership. Capable of driving projects from conception to completion, mentoring junior engineers, and helping to define and influence AI strategy and best practices. Qualifications

BACHELOR OF COMPUTER SCIENCE

Base Salary Range $100,000 to $120,000 Per Annum TCS Employee Benefits Summary Discretionary Annual Incentive. Comprehensive Medical Coverage: Medical & Health, Dental & Vision, Disability Planning & Insurance, Pet Insurance Plans. Family Support: Maternal & Parental Leaves. Insurance Options: Auto & Home Insurance, Identity Theft Protection. Convenience & Professional Growth: Commuter Benefits & Certification & Training Reimbursement. Time Off: Vacation, Time Off, Sick Leave & Holidays. Legal & Financial Assistance: Legal Assistance, 401K Plan, Performance Bonus, College Fund, Student Loan Refinancing. #J-18808-Ljbffr Tata Consultancy Services