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 / multiagent 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) AIAugmented 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 requirements in mind

8) Cost & Performance Optimization:

- Ability to design for cost-productive AI usage, including:
- Model selection and tiering
- Caching and reuse strategies
- Routing tasks to appropriate model complexity
- Awareness of token usage, OCR costs, and scaling cost drivers
- Experience implementing logging, metrics, and cost observability

9) Engineering & Delivery Skills:

- Strong Python development skills and familiarity with AI/ML ecosystems
- Ability to deliver end-to-end solutions (POC MVP production)
- Experience working in cross-functional engineering teams
- Comfortable operating as a senior individual contributor with architectural influence

10) Communication & Collaboration:

- Ability to explain complex AI systems to technical and non-technical stakeholders
- Comfortable collaborating with platform, security, and compliance teams
- Balances hands-on delivery with design leadership

📌 Generative AI/ Document Intelligence Engineer (Bengaluru)
🏢 Tata Consultancy Services
📍 Bengaluru