Senior Agentic (AI) Engineer
Worth AI
This is a senior engineering role, but it's a good early-career move if you've shipped real code and want to go deep on AI systems. You'll own agent architecture end-to-end—not just prompting, but retrieval, evaluation, and production deployment on regulated financial data. You'll partner with the Chief AI Officer and applied scientists, which means you're learning from people who set the direction.
Day-to-day, you'll design multi-step agent systems using LangGraph or similar frameworks. You'll build the retrieval layer (chunking, search, reranking), own the eval stack (golden sets, A/B tests, red-teaming), and expose agents to production via typed tools and MCP servers. You'll also handle MLOps: versioning, cost budgets, tracing, and safe fallbacks.
This role fits engineers with solid software fundamentals who've worked with LLMs, retrieval systems, or agent frameworks. A portfolio showing agent or RAG work helps. Computer science, machine learning, or adjacent backgrounds are relevant, but demonstrated shipped projects matter more than degree.
To apply, submit your résumé and a link to your work (GitHub, demos, or relevant projects) on CareerJumpShip. Be direct about what you've built with agents or AI systems.
About this role
Worth AI is hiring a Senior Agentic AI Engineer to design and ship production agent systems that automate KYB, underwriting, and risk decisions on regulated financial data. You’ll own agents end-to-end architecture, retrieval, tools, evals, and production deployment and partner closely with our Chief AI Officer, applied scientists, and platform teams. Responsibilities Design and ship multi-step agentic systems (planner/executor, tool-using, multi-agent, human-in-the-loop) for onboarding, underwriting, case review, and continuous monitoring. Architect agent graphs in LangGraph (or comparable — CrewAI, AutoGen, Claude Agent SDK) with explicit state, durable execution, retries, and safe fallbacks. Build the retrieval layer powering our agents — chunking, hybrid search, reranking, and grounded citation. Own the eval stack: golden sets, offline regression suites, LLM-as-judge, online A/B and shadow evals, and red-teaming for jailbreaks, prompt injection, and PII leakage. Expose agents to production systems via well-typed tools and MCP servers. Treat tool surface area as a product. Drive production MLOps: deployment, versioning, traffic shaping, cost/latency budgets, tracing, and on-call playbooks for agent incidents. Partner with security and compliance to keep agents inside SOC 2, GDPR, CCPA, and fair-lending posture — auditability and explainability built in, not bolted on. Mentor engineers on agent patterns, prompt hygiene, eval discipline, and LLM failure modes. Technology Stack Languages: Python, Node.js, TypeScript Agent / LLM frameworks: LangGraph, LangChain, Claude Agent SDK, MCP, OpenAI SDK Models: Anthropic Claude, OpenAI, open-weight where appropriate Retrieval & Data: PostgreSQL, pgvector, OpenSearch, Kafka, Redshift, Redis Infra: AWS, Kubernetes (EKS), ArgoCD, Terraform Evals & Observability: LangSmith / Langfuse / Braintrust-style tooling, DataDog Requirements 5+ years of software engineering experience, with 2+ years building production LLM or agentic systems (not just notebooks or demos). Hands-on experience with a modern agent framework (LangGraph strongly preferred) and a track record of shipping agents that run, fail gracefully, and recover. Strong RAG fundamentals chunking, embeddings, hybrid retrieval, reranking, grounding — and judgment about when RAG isn’t the right answer. Real eval experience golden sets, offline and online evaluations, used to make ship/no-ship calls. Production MLOps fluency: deployed LLM workloads under real latency, cost, and reliability constraints. Strong Python; comfortable in TypeScript / Node.js. Solid systems engineering instincts APIs, async patterns, queues, databases, distributed system failure modes. Calibrated communicator; thrives in ambiguous, fast-moving environments. Prior experience in fintech, lending, payments, KYB/KYC, fraud, or AML. Experience building MCP servers or other structured tool interfaces for LLMs. Background in classical ML (ranking, scoring, calibration). Experience designing explainable / auditable AI workflows for regulated environments. Open-source contributions to agent frameworks, eval tooling, or retrieval libraries. AWS depth (EKS, MSK, RDS, S3, Lambda) and IaC with Terraform. Success Metrics Agent Quality: Measurable improvements in task success rate, grounding accuracy, and hallucination rate on our eval suites. Production Reliability: Agents you own meet defined SLOs for latency (P90/P99), tool-call success, and cost per task. Velocity: New agent capabilities go from prototype to production in weeks, without skipping evals or guardrails. Risk Posture: Zero material incidents tied to prompt injection, PII leakage, or unsafe tool use on agents you own. Force Multiplier: Patterns, tools, and eval scaffolding you build get adopted across engineering. All Remote Hires will be required to travel to Orlando, Florida at least twice per year for Town Halls and team collaboration, in addition to orientation in Orlando. Benefits Health Care Plan (Medical, Dental & Vision) Retirement Plan (401k, IRA) Life Insurance Flexible Paid Time Off 9 paid Holidays Family Leave Remote Hybrid work (for Orlando Associates) Free Food & Snacks (Orlando) Wellness Resources Originally posted on Himalayas
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