Senior AI Engineer - Agentic Systems
Location: Plano, TX / Columbus, OH / New York, NY / New Jersey (5 days onsite)
Employment Type: Contract 12 months, with possible extension
Start: Immediate priority given to candidates available to interview and start quickly
Job Overview
We are a technology organization operating at the scale of a major financial enterprise, building AI agents that serve millions of customers directly. This is not a chatbot bolt-on or an innovation-lab experiment; our agentic platform sits in the production path of real customer conversations, with the reliability, latency, and safety bar that implies.
We are engaging senior engineers who have shipped agentic systems in production people who understand the difference between a demo that works and an agent that survives contact with real users. You will join a small, senior team moving at startup pace with enterprise-scale distribution.
Responsibilities
Design and build production agentic workflows for customer-facing use cases, including multi-turn conversation, tool calling, retrieval, escalation, and human handoff.
Own agent orchestration end to end using LangGraph/LangChain (or equivalent frameworks), including state management, guardrails, and failure recovery.
Integrate agents with the core service layer existing Java/Spring microservices, APIs, and event streams.
Build and tune the evaluation loop: automated evals, regression suites, trace analysis, and A/B measurement of agent quality.
Work on the inference and serving layer: model routing, prompt/context management, and self-hosted serving with vLLM alongside commercial LLM APIs.
Harden systems for a regulated, high-trust environment: PII handling, auditability, deterministic fallbacks, and observability.
Required Qualifications
7+ years of software engineering experience, with senior/lead-level ownership of production systems.
Proven, hands-on delivery of LLM-based agentic systems in production not coursework or proof-of-concept work. Candidates should be prepared to discuss the architecture, failure modes, and eval strategy of a system they have shipped.
Strong Python (primary agent/ML stack) and Java (service integration) both used daily in this role.
Depth in the standard agent stack: LangChain, LangGraph, prompt/context engineering, tool/function calling, and RAG pipelines.
Working knowledge of PyTorch and model fundamentals sufficient to fine-tune models, debug model behavior, and reason about tradeoffs.
Experience with LLM serving/inference platforms such as vLLM, TGI, or TensorRT-LLM, plus commercial APIs (OpenAI, Anthropic, Bedrock/Vertex).
Solid distributed-systems fundamentals: APIs, queues, caching, observability, and CI/CD.
Preferred Qualifications
Major Plus: Hands-on experience with agent-builder platforms such as Sierra or Decagon deploying, extending, or evaluating them for customer-service use cases.
Experience with voice agents, real-time/streaming inference, or contact-center integration.
Experience in regulated industries (finance, healthcare), including model risk and compliance review, and audit trail requirements.
Experience with eval frameworks (LangSmith, Braintrust, or custom harnesses) and LLM safety/guardrail tooling.
Additional Notes
All candidates will be evaluated for role fit prior to being submitted for client review. Candidates who can interview and start on short notice will be prioritized.
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