Gen AI Developer

About the Role

We are building next-generation GenAI capabilities for a fintech platform, and we are looking for a GenAI /Agent Developer to design and ship production-grade LLM applications. You will own the end-to-end lifecycle of Retrieval-Augmented Generation (RAG) pipelines and agentic workflows from prototype to secure, scalable deployment on AWS. This is a hands-on engineering role for someone who has moved GenAI from notebooks into real, reliable products handling sensitive financial data.

Key Responsibilities

Design, build, and maintain RAG pipelines for document ingestion, chunking, embedding, retrieval, re-ranking, and grounded generation.

Design and orchestrate

multi-step, multi-tool AI agents

that plan, call tools/APIs, and complete complex financial workflows reliably.

Develop and deploy

GenAI solutions on AWS

with production standards for scalability, observability, cost control, and security.

Implement

guardrails, evaluation, and monitoring

to ensure accuracy, safety, and hallucination control on financial and regulated content.

Integrate LLM services with internal APIs, vector stores, and knowledge/graph layers.

Use Claude Code to accelerate development, prototyping, and code review.

Partner with backend, semantic, and QA engineers to deliver features end-to-end.

Continuously benchmark models, prompts, and retrieval strategies to improve quality, latency, andcost.

Mandatory Skills

Python

strong, production-level profi ciency (must-have).

GenAI / LLM application development

— hands-on experience shipping LLM-powered features toproduction.

RAG architecture

— practical experience designing retrieval pipelines (chunking, embeddings,retrieval, re-ranking, grounding).

AWS

— building and deploying applications on AWS services.

Claude Code

— comfortable using Claude Code (or equivalent AI coding assistants) in dailydevelopment.

Nice-to-Have Skills

Vector databases
(e.g., Pinecone, Weaviate, pgvector, OpenSearch, FAISS).
Agent orchestration frameworks
(LangGraph, CrewAI, or similar).
Prompt engineering & evaluation tooling
(systematic prompt design, offl ine/online eval, LLM-as-judge, tracing).
Exposure to LLMOps, model observability, and prompt/version management.
Experience with regulated or fi nancial data (security, privacy, compliance awareness).