Staff Agentic AI Engineer
New
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NetomiEnterprise AI
Toronto , CanadaFull-TimeStaff
Salary not disclosed
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Job Details
- Experience
- 5-7+ years
- Required Skills
- AWSPythonMachine LearningNLPLLMLangChain
Requirements
- Bachelor’s Degree or higher in a quantitative field (Statistics, Computer Science, Engineering, Mathematics).
- 5-7+ years of experience in AI/ML engineering, applied machine learning, natural language processing, and/or AI systems development.
- 2+ years of hands-on experience building production LLM or AI agent systems.
- Demonstrated experience building agents used in production by external customers using LangGraph, LangChain, or related frameworks.
- Experience building systems that turn messy, unstructured source material into validated structured output using schemas or ontologies.
- Strong Python engineering skills and experience building scalable production software systems.
- Experience designing agent architectures involving tool use, reasoning flows, retrieval, memory, guardrails, and workflow orchestration.
- Experience developing AI evaluation systems, including LLM-as-judge, guardrail evaluation, and production quality measurement.
- Strong understanding of RAG, embeddings, retrieval quality, and knowledge-grounded generation.
- Experience deploying or operating systems in AWS cloud environments.
- Daily use of AI coding tools such as Codex, Claude Code, Cursor, or similar.
Responsibilities
- Design, build, and improve production-grade AI agentic systems for developing agents on Netomi’s core platform.
- Architect agent workflows involving reasoning, tool use, retrieval, guardrails, escalation paths, and performance monitoring.
- Evaluate Deep Agent, Claude Agent, OpenAI Agent, LangGraph, and other emerging agent orchestration patterns and frameworks for specific new use cases.
- Build and maintain LLM evaluation systems, including LLM-as-judge workflows, regression evals, guardrail testing, quality metrics, and production behavior analysis.
- Diagnose agent performance issues across prompts, tool selection, retrieval quality, latency, cost, task completion, and failure modes.
- Design and implement RAG and embedding-based capabilities for enterprise knowledge access and automation workflows.
- Build scalable Python services and platform components deployed in AWS cloud environments.
- Partner with product, platform, and engineering teams to translate emerging agentic AI capabilities into reliable platform features.
- Establish engineering best practices for continuous optimization of agentic systems.
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