Senior Agentic AI Engineer, Fintech & Regulated Data
Overview
Own and architect multi-step agentic systems that automate KYB, underwriting, and risk decisions on regulated financial data at scale. You will lead end-to-end architecture, retrieval, tools, evals, and production deployment in collaboration with the Chief AI Officer, applied scientists, and platform teams. You will work on agent graphs, retrieval layers, eval stacks, and production MLOps to ensure safety, compliance, and explainability in regulated environments.
What You'll Do10
- 1Build multi-step agentic systems including planner and executor components for onboarding, underwriting, case review, and monitoring.
- 2Design LangGraph-based agent graphs with state, durable execution, retries, and safe fallbacks.
- 3Own the retrieval layer powering agents with chunking, hybrid search, reranking, and grounded citations.
- 4Own the eval stack with golden sets, offline regression, LLM-as-judge, online A/B/shadow evals, and red-teaming for prompt injection and PII leakage.
- 5Expose agents to production systems via well-typed tools and MCP servers, treating tool surface area as a product.
- 6Drive production MLOps including deployment, versioning, traffic shaping, cost/latency budgets, tracing, and on-call playbooks.
- 7Partner with security and compliance to maintain SOC 2, GDPR, CCPA, and fair-lending posture with built-in auditability and explainability.
- 8Mentor engineers on agent patterns, prompt hygiene, eval discipline, and LLM failure modes.
- 9Collaborate with platform and security teams to ensure robust, auditable AI workflows in regulated environments.
- 10push for scalable, reliable agent systems that perform under real-time constraints.
Requirements10
- 15+ years of software engineering experience, with 2+ years building production LLM or agentic systems
- 2Hands-on experience with a modern agent framework (LangGraph strongly preferred) and a track record of shipping agents that run and recover gracefully
- 3Strong RAG fundamentals including chunking, embeddings, hybrid retrieval, reranking, grounding
- 4Real eval experience with golden sets, offline and online evaluations to inform ship/no-ship decisions
- 5Production MLOps fluency with deployed LLM workloads under latency, cost, and reliability constraints
- 6Strong Python skills; comfortable with TypeScript / Node.js
- 7Experience building MCP servers or other structured tool interfaces for LLMs
- 8Background in classical ML (ranking, scoring, calibration) and explainable/auditable AI workflows for regulated environments
- 9AWS depth (EKS, MSK, RDS, S3, Lambda) and IaC with Terraform
- 10Prior fintech, lending, KYB/KYC, fraud, or AML experience
Salary Insight
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