Dataeconomy
Senior / Lead GenAi Engineer
Leads a team to design, build, and deploy production-grade Generative AI and Agentic AI solutions for banking and financial services using AWS Bedrock, SageMaker, and Python.
- Compensation
- Not disclosed
- Location
- Hyderabad, India
- Work setup
- Hybrid
- Seniority
- Lead
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- 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, SEB
Full description
Job Title: Senior / Lead GenAI Engineer**
Experience Level: 8 - 15 years
Location: Hyderabad / PuneQualification: BTech/MTech/MCA
Mode of Work: Hybrid**
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.