Role Description

Key Responsibilities

- 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 — agentic developer workflows, RAG pipelines, LLM-based applications — for insurance workloads, 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 offshore 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 — with particular attention to the data privacy, security, and regulatory constraints of the insurance industry; 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.
- 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).
- Context Engineering, Business Ontology & Domain Knowledge
- Mine existing artifacts (legacy code, documentation, wikis, tickets) to bootstrap the customer's knowledge fabric; establish curation, versioning, and freshness practices so context stays accurate as systems and regulations evolve.
- Data preparation for AI: profile and cleanse source data (normalization, deduplication, OCR noise in scanned documents), structure unstructured content, and curate versioned datasets — including synthetic data where appropriate — for retrieval, evaluation, and fine-tuning.
- Data protection in AI pipelines: de-identification, PII masking, and redaction for data used in prompts, retrieval corpora, and evaluations; data lineage and access controls aligned to insurance privacy and regulatory requirements.




- Metadata management and data governance: business glossaries, data catalogs, and lineage that keep the ontology, retrieval corpora, and datasets trustworthy and traceable as systems evolve.
- Apply prompt engineering rigor: few-shot examples, chain-of-thought, structured output design, prompt versioning, and evaluation against golden datasets.

Required Qualifications

- 14–18 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, application maintenance and solution delivery, 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 responsible 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 modelling business domains and processes: ontologies, taxonomies, or knowledge graphs; state machines and lifecycle models; and the metadata practices that keep them current.
- 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 from an offshore model, and explaining complex technology in an easy-to-understand way.

Skills: Kafka, .NET, Metadata Management, Java, Spring, Node.js, Python

Experience: 14.00-18.00 Years

📌 Principal - Architecture (Hyderabad)
🏢 LTM
📍 Hyderabad