Sr AI Engineer - Software

Snowrelic Inc; Jersey City (NJ); On-site; USD 150,000 - 230,000; Full time

Job summary

Snowrelic Inc. in the United States is seeking a Sr AI Engineer to own production-grade AI agent systems in a high-scale environment, with strict reliability, latency, and safety requirements. Location: On-site across NY/NJ, Columbus, or Dallas.

The role demands hands-on leadership, deep Python/Java experience, and strong knowledge of LangChain, LLM serving, and end-to-end ML pipelines in production. You will collaborate with platform, data and product teams to design robust architectures and

Qualifications

• 7+ years of software engineering experience with senior/lead ownership of production systems.
• Proven hands-on delivery of LLM-based agentic systems in production, not POCs.
• Strong Python and Java as daily-use languages.
• Deep knowledge of LangChain, LangGraph, prompt/context engineering, tool calling, and RAG pipelines.
• Experience with LLM serving/inference (vLLM/TGI) and commercial APIs (OpenAI, Anthropic, Vertex/Bedrock).

Skills

7+ years of software engineering

Senior/lead ownership of production

LLM-based agentic systems in prod

Python

Java

LangChain/LangGraph/Prompt engineering

RAG pipelines and tooling

Job description

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.