Senior Agentic (AI) Engineer

Worth Ai; Orlando (FL); On-site; USD 120,000 - 150,000; Full time

Benefits offered by this job

Health Care Plan (Medical, Dental & Vision)

Retirement Plan (401k, IRA)

Life Insurance

Flexible Paid Time Off

9 paid Holidays

Family Leave

Free Food & Snacks

Job summary

Worth Ai is seeking a Senior Agentic AI Engineer based in Orlando, Florida. You will lead the design and deployment of multi-step agent systems for underwriting and risk decisions. The role requires strong software engineering experience and knowledge of agent frameworks.

Your responsibilities include architecting agent graphs, developing agent retrieval layers, and ensuring production MLOps. Benefits include a comprehensive healthcare plan and flexible paid time off. All remote hires will travel to Orlando for team collaboration.

Qualifications

• 5+ years of software engineering experience, with 2+ years building production LLM or agentic systems.
• Hands-on experience with a modern agent framework.
• Strong RAG fundamentals.
• Production MLOps fluency under latency, cost, and reliability constraints.
• Experience designing explainable AI workflows for regulated environments.

Responsibilities

• Design and ship multi-step agentic systems for onboarding and underwriting.
• Architect agent graphs with explicit state and execution.
• Build the retrieval layer for agent operations.
• Drive production MLOps for agent systems.
• Mentor engineers on agent patterns and practices.

Skills

Software engineering experience

Modern agent frameworks

Production MLOps fluency

Strong Python

Communication

Tools

LangGraph

PostgreSQL

AWS

Kubernetes

Terraform

Job description

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

• 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.

• 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