Applied AI Engineer
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Job Description
Applied AI Engineer developing and operating production AI systems for banking applications. Collaborating on workflows, APIs, and LLM integration in financial institutions.
Responsibilities:
- Agent orchestration frameworks for multi-step reasoning, tool use, and constraint-based problem solving across banking workflows
- RAG pipelines covering embedding generation, chunking, hybrid retrieval, and retrieval evaluation, calibrated for banking document types
- LLM integration layers connecting banking models, APIs, and knowledge bases into reliable, auditable inference workflows
- Evaluation infrastructure including behavioral contracts, regression baselines, and production observability for non-deterministic AI outputs
- Backend services and APIs powering client-facing AI products at bank-tier uptime requirements
Requirements:
- 5+ years software engineering; 2+ years building and shipping production agentic AI or RAG systems
- Agent framework experience: LangChain, LangGraph, PydanticAI, AutoGen, or Semantic Kernel
- RAG stack proficiency: embedding models, vector DBs (Pinecone, Weaviate, Milvus, FAISS), hybrid search, retrieval evaluation
- LLM integration depth: tool calling, structured outputs, multi-step reasoning, behavioral regression testing
- AI eval and observability tooling: LangSmith, RAGAS, DeepEval, Arize, Langfuse, or equivalent
- REST APIs, async Python, microservices; Azure cloud experience preferred
Benefits:
- Competitive base and meaningful equity.
- Remote (US). Occasional travel to client sites and team offsites.
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