{"id":2290656,"url":"https://alion.io/job/titan-ai-applied-ai-engineer","title":"Applied AI Engineer","company":{"id":4400751,"name":"Titan AI","domain":"titanbanking.ai","url":"https://alion.io/company/titan-ai","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"D","score":45,"open_postings":12,"ghost_share":0.917,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-13T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":[],"countries":[],"hiring_countries":["US"],"hiring_countries_total":1,"salary":{"min":200000,"max":300000,"currency":"USD","period":"year","gross":null,"usd_annual":300000},"salary_estimate":null,"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"ArangoDB","optional":false},{"name":"Arize Phoenix","optional":false},{"name":"AutoGen","optional":false},{"name":"Azure","optional":false},{"name":"DeepEval","optional":false},{"name":"Dgraph","optional":false},{"name":"FAISS","optional":false},{"name":"Function Calling","optional":false},{"name":"Hybrid Search","optional":false},{"name":"LangChain","optional":false},{"name":"Langfuse","optional":false},{"name":"LangGraph","optional":false},{"name":"LangSmith","optional":false},{"name":"LLM","optional":false},{"name":"Milvus","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Neo4j","optional":false},{"name":"Pinecone","optional":false},{"name":"Pydantic AI","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"Rest API","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"Structured Outputs","optional":false},{"name":"Tool Use","optional":false},{"name":"Weaviate","optional":false}],"status":"live","first_seen_at":"2026-05-28T00:43:07Z","employer_posted_date":"2026-05-28","last_verified_at":"2026-10-13T14:35:21Z","board_verified":true,"closed_at":null,"days_open":138,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":138},"description":"About Titan\nTitan 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.\nWhy This Role Exists\nTitan 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.\nWhat You Own\nAgent orchestration frameworks for multi-step reasoning, tool use, and constraint-based problem solving across banking workflows\nRAG pipelines covering embedding generation, chunking, hybrid retrieval, and retrieval evaluation, calibrated for banking document types\nLLM integration layers connecting banking models, APIs, and knowledge bases into reliable, auditable inference workflows\nEvaluation infrastructure including behavioral contracts, regression baselines, and production observability for non-deterministic AI outputs\nBackend services and APIs powering client-facing AI products at bank-tier uptime requirements\nWho You Are\nBackground 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.\nFluent 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.\nRequired Qualifications\n5+ years software engineering; 2+ years building and shipping production agentic AI or RAG systems\nAgent framework experience: LangChain, LangGraph, PydanticAI, AutoGen, or Semantic Kernel\nRAG stack proficiency: embedding models, vector DBs (Pinecone, Weaviate, Milvus, FAISS), hybrid search, retrieval evaluation\nLLM integration depth: tool calling, structured outputs, multi-step reasoning, behavioral regression testing\nAI eval and observability tooling: LangSmith, RAGAS, DeepEval, Arize, Langfuse, or equivalent\nREST APIs, async Python, microservices; Azure cloud experience preferred\nStrongly Preferred\nFintech, banking, or regulated industry experience\nGraph databases (Neo4j, ArangoDB, Dgraph) and MCP / connector architecture\nMulti-agent or planner-based AI architectures\nMulti-tenant SaaS with auditability and compliance requirements\nWhat Success Looks Like\nWithin 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.\nCompensation and Structure\nCompetitive base and meaningful equity.\nRemote (US). 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