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About TitanTitan builds AI software for banks: purpose-built small language models, a banking ontology, and AI bankers that financial institutions can trust. Our models outperform general-purpose LLMs by 30 to 80 percent on banking tasks. We operate under the compliance, audit, and model-risk standards that banking requires.Why This Role ExistsTitan is growing from a handful of live banking customers to thirty, then to hundreds. This role sits across the AI Toolbelt and Product Engineering lanes, owning the production AI systems that bank employees use every day — agent workflows, retrieval pipelines, and LLM integration layers. We bring a problem and expect a working solution.What You OwnAgent orchestration frameworks for multi-step reasoning, tool use, and constraint-based problem solving across banking workflowsRAG pipelines covering embedding generation, chunking, hybrid retrieval, and retrieval evaluation, calibrated for banking document typesLLM integration layers connecting banking models, APIs, and knowledge bases into reliable, auditable inference workflowsEvaluation infrastructure including behavioral contracts, regression baselines, and production observability for non-deterministic AI outputsBackend services and APIs powering client-facing AI products at bank-tier uptime requirementsWho You AreBackground in software engineering with at least five years of experience, the last two spent building and operating production AI systems. Shipped agentic workflows, RAG pipelines, or LLM-powered applications to real users. Strong Python fundamentals across APIs and async systems, which is the foundation the AI work sits on. Comfortable picking the practical solution over the clever one.Fluent in LangChain, LangGraph, PydanticAI, or AutoGen, with hands-on experience with vector databases, retrieval evaluation, and observability tooling such as LangSmith, RAGAS, Arize, or Langfuse. Prior fintech or banking experience is a genuine advantage, not a checkbox.Required Qualifications5+ years software engineering; 2+ years building and shipping production agentic AI or RAG systemsAgent framework experience: LangChain, LangGraph, PydanticAI, AutoGen, or Semantic KernelRAG stack proficiency: embedding models, vector DBs (Pinecone, Weaviate, Milvus, FAISS), hybrid search, retrieval evaluationLLM integration depth: tool calling, structured outputs, multi-step reasoning, behavioral regression testingAI eval and observability tooling: LangSmith, RAGAS, DeepEval, Arize, Langfuse, or equivalentREST APIs, async Python, microservices; Azure cloud experience preferredStrongly PreferredFintech, banking, or regulated industry experienceGraph databases (Neo4j, ArangoDB, Dgraph) and MCP / connector architectureMulti-agent or planner-based AI architecturesMulti-tenant SaaS with auditability and compliance requirementsWhat Success Looks LikeWithin 90 days, ownership of at least one production AI workflow end to end with measurable improvements shipped to the retrieval or agent layer. Within six months, the go-to person on the team for hard agent and retrieval problems, operating independently from a high-level brief through to recommendation and implementation. At one year, a senior anchor on the AI engineering function with a track record of pulling others up and a credible path to leading other AI Engineers.Compensation and StructureCompetitive base and meaningful equity.Remote (US). Occasional travel to client sites and team offsites.Compensation Range: $200K - $300K
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