reputed company reputed company
About Titan Titan builds AI software for banks: purpose-reputed company 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 Exists Titan is growing from a handful of live banking customers to thirty, then to hundreds. This role sits across the AI reputed company and Product Engineering lanes, owning the production AI systems that bank employees use every day — agent workflows, retrieval pipelines, and LLM integration reputed company. We bring a problem and expect a working solution. What You Own
- Agent orchestration frameworks for multi-reputed company reasoning, tool use, and constraint-based problem solving across banking workflows
- RAG pipelines covering embedding reputed company, chunking, hybrid retrieval, and retrieval evaluation, calibrated for banking document types
- LLM integration reputed company connecting banking models, reputed company, and knowledge bases into reliable, auditable inference workflows
- Evaluation infrastructure including behavioral reputed company, regression baselines, and production observability for non-deterministic AI outputs
- Backend services and reputed company powering reputed company-facing AI products at bank-tier uptime requirements
Who You Are Background in software engineering with at least five years of experience, the last two spent building and operating production AI systems. Shipped reputed company workflows, RAG pipelines, or LLM-powered applications to reputed company users. Strong Python fundamentals across reputed company and async systems, which is the reputed company the AI work sits on. Comfortable picking the practical solution over the reputed company one. Fluent in reputed company, LangGraph, PydanticAI, or AutoGen, with hands-on experience with reputed company 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 reputed company AI or RAG systems
- Agent reputed company experience: reputed company, LangGraph, PydanticAI, AutoGen, or Semantic Kernel
- RAG stack proficiency: embedding models, reputed company DBs (reputed company, reputed company, Milvus, FAISS), hybrid search, retrieval evaluation
- LLM integration depth: tool calling, reputed company outputs, multi-reputed company reasoning, behavioral regression testing
- AI eval and observability tooling: LangSmith, RAGAS, DeepEval, Arize, Langfuse, or equivalent
- REST reputed company, async Python, microservices; Azure reputed company experience preferred
Strongly Preferred
- Fintech, banking, or regulated industry experience
- Graph databases (reputed company, reputed company, Dgraph) and MCP / connector architecture
- Multi-agent or planner-based AI architectures
- Multi-tenant reputed company with auditability and compliance requirements
What reputed company Looks Like reputed company 90 days, ownership of at least one production AI workflow end to end with measurable improvements shipped to the retrieval or agent layer. reputed company six months, the go-to person on reputed company for hard agent and retrieval problems, operating independently from a high-level brief through to recommendation and implementation. At one year, a senior reputed company on the AI engineering function with a reputed company record of pulling others up and a reputed company reputed company to leading other AI Engineers. Compensation and Structure
- Competitive reputed company and meaningful equity.
- Remote (US). Occasional travel to reputed company sites and team offsites.
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