Senior/Lead GenAi Engineer

DATAECONOMY

Senior / Lead GenAI Engineer architecting, building, and leading delivery of Generative AI solutions in Banking & Financial Services. Leadership and hands-on development in a hybrid work environment.

Posted 7/17/2026full-timeHyderabad • 🇮🇳 IndiaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying GenAI solutions with a strong focus on AWS cloud architecture, RAG pipeline design, and technical leadership. Proven ability to mentor teams and translate complex BFSI requirements into scalable applications while ensuring compliance with regulatory standards.

Highest-signal resume keywords

Amazon Bedrock ExpertiseAgentic AI DevelopmentRAG Pipeline DesignPython ProficiencyTechnical Leadership

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

GenAI Application DevelopmentRAG ImplementationPrompt EngineeringAPI DevelopmentCloud-Native SolutionsModel Evaluation FrameworksMulti-Agent SystemsVector Database IntegrationProduction-Grade CodeInfrastructure as Code

Soft Skills

MentoringCollaborationProblem-SolvingCommunicationTechnical Documentation

Tools & Technologies

AWS BedrockSageMakerLambdaTerraformBoto3FastAPIPineconeOpenSearchWeaviateAgentCore

Industry Keywords

BFSIRegulatory ComplianceFraud DetectionRisk AnalyticsCustomer EngagementAMLCredit RiskRBISEBIGDPR

Tech Stack

Tools & technologies

AWSCloudDynamoDBPythonTerraform

About the role

Key responsibilities & impact

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

What you’ll need

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).

Benefits

Comp & perks

Comprehensive Medical Coverage: Health insurance of INR 5.0 Lakhs for you and your family (up to 6 members), ensuring complete peace of mind.
Robust Protection Plans: Group Personal Accident Insurance and Group Term Life Insurance to safeguard you and your loved ones.
Retirement Benefits: PF and Gratuity provided as per standard government regulations.
Flexible Work Options: Enjoy hybrid work arrangements & flexible working hours
Generous Leave Policy: 21 days of annual leave, in addition to 10 company-declared holidays.
Employee Well-being Spaces: Access to a dedicated break-out area with round-the-clock refreshments for relaxation and rejuvenation.