# Senior Agentic (AI) Engineer

**Company:** [Worth AI](https://hotfix.jobs/companies/worth-ai)
**Location:** Remote
**Role:** ML Engineering
**Experience:** 5+ years
**Skills:** LangGraph, LangChain, Python, TypeScript, Node.js, RAG, MLOps, Kubernetes, AWS, Postgres, Pgvector, Opensearch, Kafka, Redis, Terraform
**Posted:** 2026-05-15

> Designs and deploys production agentic AI systems using LangGraph for automating KYB, underwriting, and risk decisions in fintech. Requires 5+ years engineering with 2+ years in production LLMs/agents, strong RAG/evals/MLOps, Python/TypeScript.

## Job Description

## 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.
- Mentor engineers on agent patterns, prompt hygiene, eval discipline, and LLM failure modes.

## 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.

## Nice-to-Haves
- 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**.

## 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

## Benefits
- Health Care Plan (Medical, Dental & Vision)
- Retirement Plan (401k, IRA)
- Life Insurance
- Flexible Paid Time Off
- 9 paid Holidays
- Family Leave

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