GenAI Full Stack Lead Engineer - Python/React.js

JOB DESCRIPTION : Open Position : GenAI Full-Stack Lead Engineer

Experience : 4 to 8 Years

Must-Have : (React and/or Next.js) + (Node.js and/or Python) + AWS + LLM + NLP

Shift Time : 2 : 00 PM to 11 : 00 PM (IST)

Position Overview :

We are seeking a GenAI Full-Stack Lead Engineer with strong software engineering fundamentals and the ability to build production-grade frontend, backend, and GenAI-driven systems. The ideal candidate has deep expertise in RAG pipelines, LLM APIs, Agentic LLM architectures, and cloud deploymentespecially on AWS Bedrock. Prior FinTech experience is mandatory.

Key Responsibilities : 1. Full-Stack Engineering :

• Build scalable user-facing applications using React, Next.js, or similar frameworks.

• Develop backend services in Node.js or Python, including REST and GraphQL APIs.

• Implement secure authentication, authorization, and integration patterns.

• Ensure performance, reliability, and high code quality across the stack.

2. GenAI Systems & Agentic Architectures :

• Design and implement end-to-end RAG pipelinesincluding ingestion, chunking, embeddings, vector DB indexing, and retrieval tuning.

• Integrate and orchestrate LLM APIs (OpenAI, Anthropic, AWS Bedrock models, etc.).

• Build LLM Agents and Agentic workflows for multi-step reasoning, tool use, task delegation, and autonomous execution.

• Develop internal frameworks for prompts, guardrails, caching, evaluation, and fallback logic.

• Optimize LLM latency, cost, safety, and output reliability.

3. Cloud Engineering & DevOps :

• Deploy and scale applications on AWS using services such as AWS Bedrock, Lambda, ECS/EKS, S3, API Gateway, DynamoDB, and RDS.

• Build CI/CD pipelines, automate deployments, and manage staging/production environments.

• Implement observability (logging, metrics, tracing) and cost optimization.

• Work with vector databases such as Pinecone, Weaviate, Chroma, and Milvus.

4. FinTech Product Development :

• Build GenAI features for financial workflows with high accuracy, security, compliance, and auditability.

• Work with structured and unstructured financial data, documents, and transactional datasets.

• Ensure AI systems follow FinTech domain rules, constraints, and regulatory requirements.

• Collaborate with product, risk, compliance, and cross-functional engineering teams.