Qualification: BTech/MTech/MCA

We are seeking a high-calibre Senior Lead GenAI Engineer to architect, build, and lead the delivery of production-grade Generative AI and Agentic AI solutions within our Banking & Financial Services platform. You will combine deep hands-on engineering with technical leadership - guiding a team of GenAI engineers, contributing production-quality code, and partnering with Solution Architects and business stakeholders to bring cutting-edge AI capabilities into real financial products at scale.

Key Responsibilities:

- Lead a team of GenAI engineers - providing technical leadership, architecture guidance, mentoring, and code reviews to drive engineering excellence and delivery quality.

- Actively contribute production-quality code - this is a hands-on role; you will design, develop, and deploy GenAI solutions alongside the team, not just guide from the sidelines.

- Design and deploy production-grade GenAI and Agentic AI applications - secure, scalable, compliant, and aligned with BFSI regulatory requirements (RBI, SEBI, GDPR).

- Build AI agents and multi-agent workflows using Amazon Bedrock, Bedrock Agents, AgentCore, and modern agent orchestration frameworks - enabling autonomous, multi-step financial AI processes.

- Design and implement RAG-based solutions - including vector database integration, chunking strategies, retrieval optimisation, prompt engineering, AI guardrails, and model evaluation frameworks.

- Develop reusable frameworks, libraries, and accelerators - standardising GenAI engineering practices and improving delivery velocity across the team.

- Design and implement cloud-native solutions on AWS - using services such as Bedrock, SageMaker, Lambda, S3, and Infrastructure as Code (Terraform) for scalable, production-ready deployments.

- Collaborate with Solution Architects, Product Owners, and business stakeholders - translating complex BFSI requirements (fraud detection, risk analytics, compliance, customer engagement) into scalable GenAI solutions.





- Troubleshoot complex technical issues - from hallucination rates and retrieval quality to latency, cost optimisation, and production incidents - guiding teams through design and implementation challenges.

- Produce high-quality technical documentation - including architecture documents, HLDs, LLDs, API documentation, deployment guides, and operational runbooks for audit and knowledge sharing.

- Stay current with emerging AI technologies - evaluating new models, frameworks, and approaches; recommending improvements to architecture, engineering practices, and solution design.

Requirements

- Amazon Bedrock expertise: deep hands-on experience with Bedrock, Bedrock Agents, AgentCore, Knowledge Bases, and Bedrock Guardrails for production GenAI applications.

- Agentic AI development: proven experience building multi-agent systems and autonomous workflows using LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or AWS-native agent frameworks.

- RAG pipeline design: end-to-end RAG implementation - vector databases (Pinecone, OpenSearch, Weaviate, pgvector), embedding models, retrieval strategies, and evaluation using RAGAS or equivalent.

- Prompt engineering mastery: advanced prompting techniques - few-shot, chain-of-thought, structured outputs, system prompt design, and prompt versioning for production systems.

- Python proficiency: production-grade Python for GenAI applications, API development (FastAPI), and integration with AWS services using Boto3.

- AWS cloud architecture: practical experience with AWS services - Bedrock, SageMaker, Lambda, S3, DynamoDB, API Gateway, ECS/EKS - and Terraform for IaC.

- LLM evaluation and observability: model evaluation frameworks, A/B testing, hallucination monitoring, latency tracking, cost optimisation, and LLMOps practices in production.

- Technical leadership: proven track record leading engineering teams - architecture reviews, code reviews, mentoring, and driving delivery standards in an Agile setting.

- BFSI domain understanding: familiarity with banking and financial services use cases - fraud detection, AML, credit risk, regulatory reporting, customer analytics, and compliance constraints (RBI, SEBI, GDPR).

📌 Lead GenAI Engineer (Hyderabad)
🏢 Data Economy
📍 Hyderabad