Applied AI Engineer
This is the employer's own posting, not a copy on a job board.
What we know
Is it still open?
Confirmed still open
Last checked 2d ago — checked against the employer's own applicant tracking system, which is the company answering directly.
We re-read the employer's own applicant tracking system and the posting was still there. That is the company answering directly.
How old is it?
Posted 158d ago
The date the source published, not the day we noticed it (2026-05-28). Last seen at its source 7h ago.
We have tracked this listing since 3 Sep 2026 (59 days). The employer's own board has carried it every time we have read it, most recently 7 hours ago.
Is it remote?
Marked remote on the employer's board
Their board carries a remote setting on this posting — a field they filled in, not wording we read. The location field names somewhere specific, which is usually where the team or the entity sits.
Who may apply?
United States
The description agrees: it names United States.
What the ad says
…Remote (…) US…
Pay
$200k–300k/yr
Published by the source in its own salary field. Shown in the posting's own currency and period; we never convert.
Skills named in the ad
Recognised terms only, from a fixed vocabulary — this is what CV matching compares against.
Carried by 1 source
-
Ashby employer's own board first seen 59d ago · last seen 7h ago
The listing
Titan 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 Own• 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
Who 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 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; Azure cloud experience preferred
Strongly Preferred• 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
What 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 Structure• Competitive base and meaningful equity.
• Remote (US). Occasional travel to client sites and team offsites.