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Senior Agentic (AI) Engineer at Worth AI · globalremotely

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