Applied AI Engineer

The role

This person will own production AI systems including agent orchestration frameworks, RAG pipelines, and LLM integration layers. They will build backend services and APIs for client-facing AI products used by bank employees.

What you will do

• Agent orchestration frameworks for multi-step reasoning, tool use, and constraint-based problem solving
• RAG pipelines covering embedding generation, chunking, hybrid retrieval, and retrieval evaluation
• LLM integration layers connecting banking models, APIs, and knowledge bases
• Evaluation infrastructure including behavioral contracts, regression baselines, and production observability
• Backend services and APIs powering client-facing AI products

Required qualifications

• 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

Nice to have

• Azure cloud experience
• Fintech, banking, or regulated industry experience
• Graph databases (Neo4j, ArangoDB, Dgraph) and MCP / connector architecture
• Multi-agent or planner-based AI architectures
• Multi-tenant SaaS with auditability and compliance requirements

Benefits

• Competitive base
• Meaningful equity

About the company

Titan builds AI software for banks, including purpose-built small language models, a banking ontology, and AI bankers. Their models outperform general-purpose LLMs by 30 to 80 percent on banking tasks.