Generative AI/ Document Intelligence Engineer

Tata Consultancy Services; Bengaluru; On-site; INR 4,000,000 - 6,000,000; Full time

Job summary

Tata Consultancy Services is seeking a senior AI/ML specialist in Bengaluru with 12–15 years of IT experience to design and scale GenAI-powered solutions. You will work on OCR/document intelligence, NLP features, and multi-agent architectures, aiming for production-grade, cost-aware architectures across cloud-native environments.

Applicants should be proficient in Python, cloud services (AWS preferred), and AI integration patterns, delivering end-to-end platforms with strong emphasis on

Qualifications

• 12–15 years of IT experience (mandatory).
• Candidates with <12 years will not be considered.
• Experience building GenAI solutions and AI integration in enterprise systems.

Skills

GenAI solutions

OCR & document intelligence

NLP concepts

Multi-agent architectures

Model accuracy & explainability

End-to-end AI platforms

RAG architectures

Event-driven/serverless

Trade-offs analysis

AWS cloud

Object storage (S3)

Serverless (Lambda)

Infrastructure as Code

Cloud-agnostic design

Prompt engineering

Python development

Tools

Terraform

Job description

Experience Range: - 12 To 15 Years (Mandatory)

(Note: Candidates below 12 years of IT experience shall not be considered)

JOB DESCRIPTION

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
• Proven ability to design end-to-end AI platforms, beyond proof-of-concepts
• 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)
• 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
• 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
• 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

15 years of Full Time Education