Role description

Key Responsibilities

1. GenAI Solution Architecture & SDLC Acceleration

- Define the target-state architecture and adoption roadmap for AI-led SDLC: where and how AI agents participate in planning, coding, review, testing, and deployment phases.
- Extend agents upstream into engineering design: spec-driven development (specs and design documents as the source of truth agents implement against), AI-assisted architecture documentation, and agent reviewers in design and code-review gates.
- Architect end-to-end GenAI solutions for the customer — agentic developer workflows, RAG pipelines, LLM-based applications — and scale successful patterns from pilot to production.
- Lead architecture transformation accelerated by AI — core/legacy modernization and new event-driven architecture (EDA) implementations — using agents for code comprehension, documentation, migration, functional-equivalence test generation, and event schema/handler scaffolding.
- Design AI-enabled business workflows with humans in the loop: document intake and extraction, confidence thresholds and exception routing, reviewer feedback loops, and end-to-end auditability.
- Enable and coach developer squads on agent-assisted engineering; define working practices, quality gates, and guardrails for AI-generated code.
- Lead a team of GenAI engineers delivering these solutions — setting technical direction, reviewing designs, and growing the team's agentic engineering capability.
- Measure and report acceleration outcomes: cycle time, throughput, quality, and adoption metrics — and iterate the blueprint based on evidence.
- Define and enforce architecture standards, design patterns, and responsible-AI practices (security, data privacy, auditability) for GenAI systems; operate architecture governance — design authority, decision records, and reviews — across delivery teams.
- Establish evaluation and guardrails as first-class engineering: evaluation harnesses and golden datasets, hallucination and regression checks, output guardrails, and production monitoring for GenAI systems.

2. Agentic Engineering & Tooling (hands-on)

- Design multi-agent systems with defined roles — orchestrator, coder, reviewer, tester agents — including task routing, state management, and human-in-the-loop checkpoints.
- Hands-on configuration and governance of GenAI developer tooling at enterprise scale: agentic coding tools — CLI-driven coding agents and AI pair-programming solutions that go well beyond autocomplete-style copilots — including tool-server (MCP) setup, custom commands, hooks, and repository context/instruction files, plus agent SDKs and orchestration frameworks.
- Design and deploy MCP (Model Context Protocol) servers to expose the customer's tools, APIs, and data sources to AI agents; apply tool-use / function-calling patterns for LLM-driven agents.
- Select and integrate foundation models (e.g., Anthropic Claude, OpenAI GPT, Gemini) via APIs or managed platforms (AWS Bedrock,



Azure OpenAI / AI Foundry, Google Vertex AI); design model-tiering and routing strategies — frontier reasoning models for complex work, fast lightweight models for high-volume steps, open-weight models (Llama, Mistral) where data residency requires — balancing capability, cost, and latency.
- Deploy and operate GenAI workloads within enterprise cloud estates: private endpoints and network isolation, identity and access management, quota / rate-limit and regional-availability planning, model gateways, and cost governance (token budgeting, caching, chargeback).
- Advanced retrieval patterns: GraphRAG / knowledge-graph-augmented retrieval, text-to-SQL over enterprise data.

3. Context Engineering & Domain Knowledge Capture

- Design and operate context engineering frameworks: repository instruction and context files, system prompts, retrieval strategies, context-window budgeting, and memory patterns — so agents carry accurate project, codebase, and domain context.
- Context engineering for business ontology: model the customer's domain as formal ontologies, taxonomies, and knowledge graphs — entities, relationships, and business rules — so agents reason over structured domain semantics, not just retrieved text.
- Model business processes as state machines: explicit lifecycle and state-transition models that let agents know where a case stands and which actions are legal — and deterministic state-machine orchestration to govern non-deterministic agent workflows.
- Capture and codify insurance domain knowledge: work with SMEs to elicit product structures, underwriting and claims rules, processes, glossaries, and regulatory constraints, and convert them into machine-usable assets — knowledge bases, taxonomies, RAG corpora, and golden datasets that ground agent output.
- Apply prompt engineering rigor: few-shot examples, chain-of-thought, structured output design, prompt versioning, and evaluation against golden datasets.

Required Qualifications

- 12–14 years of proven experience in technology leadership / principal or application architect roles on enterprise-scale, distributed multi-tier systems.
- Strong architecture pedigree: n-tier, microservices, and event-driven architecture (EDA) design — including messaging/streaming platforms (Kafka or cloud-native equivalents) — enterprise integration, API design and management, and cloud-native delivery on AWS, Azure, or GCP (IaaS/PaaS/SaaS, containers, CI/CD).
- Deep expertise in at least one major enterprise stack (Java/Spring, .NET, Python, or Node.js)



with breadth across others; working proficiency in Python for GenAI development.
- Demonstrable agentic GenAI delivery: at least one agent-assisted engineering or LLM-based solution taken into production or a serious enterprise pilot — able to walk through the architecture, trade-offs, and measured outcomes.
- Practical, current knowledge of multi-agent design, MCP, RAG variants (hybrid search with re-ranking, agentic RAG, RAG over code and structured data), embeddings/vector search, prompt and context engineering, evaluation harnesses and guardrails, and LLM limitations (hallucination, context-window constraints, cost/latency, data privacy).
- Experience leading technology-driven programs — POCs, innovation initiatives, and solution asset development — through to large-scale delivery.
- Experience practicing Design Thinking and Systems Thinking in real-world scenarios.
- Outstanding client-facing communication: proven experience engaging customer stakeholders on requirements and delivery, and explaining complex technology in an easy-to-understand way.

Skills

Mandatory Skills : AI/GenAI Research, AI_Implementation_Infra - Vector Store DB, RAG, Application Architecture, Python
Positive to Have Skills : Agile (Value-Driven Delivery), AI/ML Awareness Testing, AI/ML Testing, Application Security (application security framework/ threat modelling/ Secure SDLC/ DevSecOps/Application Security Architecture Review), Generative AI/Open AI/Vector DB, GitHub Co-Pilot, LLMOps, MLOPS, MLOPS - Python, Python - Data Science, RAGAS (Retrieval Augumented Generation Assessment) Framework

About LTM
LTM is an AI-centric global technology services company and the Business Creativity partner to the world’s largest and most disruptive enterprises. We bring human insights and intelligent systems together to help clients create greater value at the intersection of technology and domain expertise. Our capabilities span integrated operations, transformation, and business AI — enabling new ways of working, new productivity paradigms, and new roads to value. Together with over 87,000 employees across 40 countries and our global network of partners, LTM — a Larsen & Toubro company — owns business outcomes for our clients, helping them not just outperform the market, but to Outcreate it. Please also note that neither LTM nor any of its authorized recruitment agencies/partners charge any candidate registration fee or any other fees from talent (candidates) towards appearing for an interview or securing employment/internship. Candidates shall be solely responsible for verifying the credentials of any agency/consultant that claims to be working with LTM for recruitment. Please note that anyone who relies on the representations made by fraudulent employment agencies does so at their own risk, and LTM disclaims any liability in case of loss or damage suffered as a consequence of the same. Recruitment Fraud Alert - https://www.ltm.com/careers/recruitment-fraud-alert

📌 Principal - Architecture (Chennai)
🏢 LTM
📍 Chennai