Sr AI Engineer
Level Senior Engineer (VP-equivalent)
The client is 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: the client's agentic platform sits in the production path of real customer conversations, with the reliability, latency, and safety bar that implies.
Location: NY/NJ, Colombus or Dallas (On-Site)
Mandatory Skills
- 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, not POCs. The interview covers the architecture, failure modes, and eval strategy of a shipped system.
- Strong Python (primary agent/ML stack) and Java (service integration) both are daily-use languages here.
- Depth in the standard agent stack: LangChain, LangGraph, prompt/context engineering, tool/function calling, RAG pipelines.
- Working knowledge of PyTorch and model fundamentals enough to fine-tune, debug model behavior, and reason about tradeoffs.
- Experience with LLM serving/inference vLLM or comparable (TGI, TensorRT-LLM), plus commercial APIs (OpenAI, Anthropic, Bedrock/Vertex).
- Solid distributed-systems fundamentals: APIs, queues, caching, observability, CI/CD.
Major Plus
- Hands-on experience with agent-builder platforms such as Sierra or Decagon deploying, extending, or evaluating them for customer-service use cases.
- Voice agents, real-time/streaming inference, or contact-center integration.
- Experience in regulated industries (finance, healthcare) model risk, compliance review, audit trails.
- Eval frameworks (LangSmith, Braintrust, custom harnesses) and LLM safety/guardrail tooling.