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 solid 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-efficient 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 Minimum Qualification:

15 years of Full Time Education Interested candidates kindly share your updated resume to [email protected] along with filling the following details to maximize your chances of getting calls from TCS.

The details are as follows: Section 1: Basic Details:

Candidate Name

Email ID

Phone Number

Current Location (City)

Preferred Location (City): Section 2: Educational Details:

Highest Regular Full‑Time Qualification

(Diploma / Graduation / Post Graduation / Others)

10th Completion Year

12th / Intermediate Completion Year

Graduation Completion Year

Post‑Graduation Completion Year (if applicable) Section 3: Work Experience:

Current Company Name

Total Experience (in years)

Number of Companies Worked

Employment Timeline

(Example: Company A – 2021 to Present, Company B – 2019 to 2021) Section 4: Compensation Details: Current CTC (in LPA)

Expected CTC (in LPA)

Notice Period

(Immediate / 15 Days / 30 Days / 60+ Days) Section 5: Documentation & Compliance (Yes / No): Valid ID Proof Available (Aadhar & Pan Card): (Yes/No)

Educational Documents Available: (Yes/No)

Employment Documents Available: (Yes/No)

UAN Number Available: (Yes/No)

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