Generative AI/ Document Intelligence Engineer

TCS Hiring for Generative AI/ Document Intelligence Engineer Experience Range: - 12 To 15 Years (Mandatory) (Note: Candidates below 12 years of IT experience shall not be considered) JOB LOCATION: Bengaluru

Key Skills: 1) Core AI & ML Skills: • Hands-on experience building GenAI solutions (LLMs, RAG pipelines, embeddings, semantic search) • Practical use of OCR and document intelligence techniques across unstructured data (PDFs, images, scanned forms) • Strong understanding of NLP concepts (entity extraction, classification, keyword detection) • Experience with agentic / multi‑agent architectures and workflow-based AI systems • Ability to adapt or fine-tune models for accuracy, confidence scoring, and explainability 2) Architecture & System Design: • Proven ability to design end-to-end AI platforms, beyond proof-of-concepts • Experience with large-scale document pipelines (ingestion → processing → indexing → retrieval) • Strong knowledge of RAG vs alternative architectures (hybrid search, knowledge graphs, semantic indexing) • Experience with event-driven and serverless patterns for scalable processing • Ability to reason about trade-offs (accuracy vs cost, latency vs scale, complexity vs maintainability) 3) Cloud & Platform Engineering: • Strong experience in at least one major cloud platform (AWS preferred) • Familiarity with: • Object storage (e.g. S3) • Serverless compute (e.g. Lambda) • Managed AI/ML and OCR services • Infrastructure-as-Code mindset (e.g. Terraform or equivalent) • Ability to design cloud-agnostic solutions where required 4) AI‑Augmented Engineering (Prompt Coding & AI Pairing): • Strong ability to use prompt engineering / prompt coding to generate, debug, and accelerate production-quality code • Demonstrated capability to pair-program effectively with AI tools, iterating prompts and validating outputs • Ability to apply judgement on when to rely on vs avoid AI-generated code, especially for security or critical logic • Experience integrating AI into engineering workflows (test generation, documentation, code reviews) • Maintains strong engineering fundamentals and code quality standards while leveraging AI as a productivity multiplier 5) MCP AI Integration (Model, Context, Platform Integration): • Experience integrating AI models into enterprise systems using API-first and service-oriented architectures • Ability to design model orchestration layers that connect LLMs, tools, data sources, and workflows (e.g. retrieval systems, APIs, event streams) • Strong understanding of context injection patterns (prompt construction, metadata enrichment, grounding, tool usage) • Experience building scalable integration pipelines between AI services and enterprise platforms (e.g. ECM systems, data lakes, APIs) • Awareness of security, governance, and compliance controls in AI integration (PII handling, access control, audit logging, isolation boundaries) 6) Production Readiness & Operations: • Clear understanding of production-ready AI systems, including: • Monitoring and alerting • Reliability and resilience • Scalability and performance • Observability and runtime support • Experience integrating into CI/CD and DevSecOps pipelines • Awareness of security scanning, vulnerability management, and secure deployments 7) Responsible AI & Risk Awareness: • Strong grounding in responsible AI principles, including: • Governance and auditability • Explainability and transparency • Bias and fairness considerations • Human-in-the-loop controls • Experience working in regulated or high-risk environments • Ability to design solutions with compliance and audit