Lead GenAI Engineer

Data Economy; Hyderabad, Pune District; On-site; INR 3,500,000 - 6,500,000; Full time

Benefits offered by this job

AI/GenAI leadership role

Job summary

Data Economy in Hyderabad seeks a Senior Lead GenAI Engineer to architect and deliver production-grade Generative AI solutions for Banking & Financial Services. You will lead a team of GenAI engineers, contribute hands-on code, and ensure secure, scalable deployments on AWS using Bedrock, SageMaker, and Terraform.

You will translate complex BFSI requirements into scalable GenAI applications, implement multi-agent workflows, and establish robust evaluation, guardrails, and operational runbooks

Qualifications

• Deep hands-on Bedrock with agents and guardrails for production GenAI apps.
• Experience building multi-agent systems and autonomous workflows.
• End-to-end RAG design with vector DBs and retrieval strategies.
• Prompt engineering mastery for production systems.
• Python for GenAI apps, FastAPI, and AWS integration.

Responsibilities

• Lead a team of GenAI engineers, provide architecture guidance, mentorship and code reviews.
• Contribute production-grade code and deploy GenAI solutions with the team.
• Design and deploy scalable GenAI and Agentic AI applications meeting BFSI regulatory requirements.
• Build AI agents and multi-agent workflows using Bedrock and related frameworks.
• Design RAG pipelines, vector stores, and retrieval optimisations.
• Develop reusable frameworks, libraries, and accelerators for GenAI engineering.
• Create cloud-native deployments on AWS using Bedrock, SageMaker, Lambda, S3 and Terraform.
• Collaborate with architects, product owners and stakeholders to translate BFSI needs.
• Troubleshoot hallucinations, latency, cost, and production incidents; document architectures.

Skills

Leadership

GenAI engineering

Cloud architecture

Regulatory compliance understanding

Team mentoring

Agile delivery

Problem solving

Architecture reviews

BFSI domain awareness

Education

BTech/MTech/MCA

Tools

Amazon Bedrock

Bedrock Agents

AgentCore

LangChain

LangGraph

LlamaIndex

CrewAI

AutoGen

AWS services

Terraform

Job description

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 environment.
• 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).