Senior GenAI Architect Agentic AI & LLM Platforms

Job Title: Senior GenAI Architect / Lead Agentic AI & LLM Platforms

Location: Chennai

Experience: 8+ years (with 3+ years in GenAI/LLM ecosystems)

Role Overview

• We are looking for a highly experienced, hands-on GenAI Architect/Lead to drive enterprise-wide adoption of Generative AI and building Agentic AI solutions. This role combines deep technical expertise, solution architecture to enable GenAI-led transformation across the SDLC and business workflows.
• You will work closely with engineering teams, architects, product owners, and leadership to design and implement scalable, production-grade GenAI systems, while also acting as a thought leader and change agent for GenAI adoption.

Key Responsibilities

Technical Leadership & Delivery

• Lead a team of GenAI developers and backend engineers to deliver enterprise GenAI applications and accelerators.
• Translate business requirements into technical designs, work breakdown, implementation plans, and engineering tasks.
• Review architecture, code, prompt design, RAG pipeline design, data model, API design, and integration approaches.
• Ensure quality, performance, security, maintainability, observability, cost efficiency, and production readiness.

Advanced RAG, Semantic Search & Knowledge Architecture

• Design and optimize RAG pipelines including ingestion, chunking, metadata extraction, embeddings, vector indexing, hybrid retrieval, re-ranking, citation generation, and answer synthesis.
• Implement semantic search across structured, semi-structured, and unstructured knowledge sources.
• Integrate with enterprise knowledge repositories such as SharePoint, Confluence, S3, Google Drive, databases, ITSM tools, and document management systems.
• Define knowledge freshness, access-controlled retrieval, lineage, deduplication, feedback loops, and retrieval quality improvement mechanisms.

Agentic AI & MCP-based Tool Integration

• Design and implement agentic workflows using LangGraph, LangChain, CrewAI, AutoGen, LlamaIndex, or equivalent.
• Implement tool calling, memory, workflow state, human-in-the-loop approval, API actions, and safe execution boundaries.
• Build or guide MCP server implementations for secure agent-to-tool and LLM-to-enterprise-system integration.
• Evaluate A2A communication patterns for multi-agent collaboration, delegation, and hand-offs where relevant.

Backend, API, Data & Event-Driven Engineering

• Build scalable services using Python, FastAPI, Node.js, Java, or similar technologies.
• Design REST APIs and microservices for LLM orchestration, semantic search, ingestion, agent execution, feedback, and evaluation.
• Use SQL and NoSQL databases for application metadata, session history, audit logs, feedback, configurations, and workflow state.
• Design Redis caching patterns for session state, memory, rate limiting, token optimization, retrieval caching, distributed locks, and workflow continuity.
• Design asynchronous workflows using Kafka, RabbitMQ, Azure Service Bus, AWS SQS/SNS, Google Pub/Sub, or equivalent, including retry, DLQ, idempotency, ordering, and schema management.

Evaluation, Observability, Security & Governance

• Implement LLM tracing, prompt/version tracking, token usage monitoring, latency tracking, cost monitoring, and feedback analytics.
• Define evaluation datasets and metrics for faithfulness, groundedness, answer relevance, context relevance, hallucination rate, toxicity, latency, and cost.
• Use LangSmith, Langfuse, RAGAS, DeepEval, TruLens, Promptfoo, OpenTelemetry, Grafana, Prometheus, ELK, Datadog, or equivalent.
• Implement guardrails against prompt injection, jailbreaks, data leakage, unauthorized tool execution, toxic responses, and privacy violations.
• Support AI-assisted SDLC adoption using GitHub Copilot, Codex, CursorAI, Claude Code, Cline, or similar tools.

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Required Skills

• Enterprise GenAI Architecture (Expert)
  
• Design and implementation of GenAI reference architectures
• Platform strategy and governance
• Reusable accelerators and frameworks
• Non-Functional Requirements (NFRs)
• Enterprise-scale AI solution design
• RAG & Semantic Search (Expert)
  
• Hybrid Search
• BM25 Search
• Vector Databases
• Embeddings
• Re-ranking
• Citations and Grounding
• Agentic AI (Expert)
  
• LangGraph
• LangChain
• CrewAI
• AutoGen
• Multi-agent systems
• Planning and memory management
• Human-in-the-loop workflows
• MCP & A2A (Architect-Level Expertise)
  
• Model Context Protocol (MCP)
• MCP Servers and Gateways
• Tool Ecosystem Integration
• Secure Tool Execution
• Agent-to-Agent (A2A) Communication Patterns
• Multi-Agent Orchestration
• Backend APIs (Strong Hands-on)
  
• Python
• FastAPI
• Node.js
• Java
• REST APIs
• Microservices Architecture
• OpenAPI
• API Gateway Design
• Observability (Expert)
  
• LangSmith
• Langfuse
• RAGAS
• DeepEval
• Promptfoo
• OpenTelemetry
• Grafana
• Prometheus
• ELK Stack or equivalent monitoring solutions
• Data Architecture (Expert)
  
• SQL & NoSQL Databases
• Vector Databases
• Object Storage
• Analytics Stores
• Metadata Management
• Data Lineage
• Audit and Compliance Tracking
• Caching (Expert)
  
• Redis
• Session Continuity
• Memory Management
• Rate Limiting
• Distributed Locks
• Retrieval Caching
• Messaging & Event Processing (Expert)
  
• Kafka
• RabbitMQ
• Event-Driven Architecture
• Dead Letter Queue (DLQ)
• Retry Mechanisms
• Idempotency Patterns
• Event Schema Management
• Asynchronous Workflows
• Cloud & Model Strategy (Expert)
  
• Azure OpenAI
• AWS Bedrock
• Google Vertex AI
• OpenAI
• Anthropic Claude
• Google Gemini
• Hugging Face
• Small Language Models (SLMs)
• LLMOps / MLOps / DevOps (Expert)
  
• Prompt and Model Versioning
• Model Evaluation Frameworks
• CI/CD Pipelines
• Docker
• Kubernetes
• Helm Charts
• Terraform
• Deployment Automation
• Security & Responsible AI (Expert)
  
• AI Guardrails
• Prompt Injection Prevention
• LLM Shielding
• Privacy and Compliance Controls
• Access Management
• Audit Logging
• Model Risk Management
• Responsible AI Governance

Soft Skills & Leadership

• Excellent communication and stakeholder management skills
• Proven ability to drive adoption and influence teams
• Strong problem-solving and architectural thinking
• Experience in mentoring teams and leading technical initiatives