Gen AI Developer

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). What We Look For A builder who ships: you have taken GenAI features from concept to production, not just demos. Strong grasp of LLM failure modes (hallucination, prompt injection, drift) and how to mitigate them. Comfort with ambiguity and rapid iteration in a fast-moving product environment. Clear communication and collaboration across engineering and product teams. Fintech Context Because this is a fintech platform, awareness of data security, PII handling, and responsible-AI practices for regulated environments is highly valued.