Experience building eval sets from production traces and synthetic data, and running structured experimentation (A/B tests, ablations, offline evals) to compare prompts, models, or agent architectures
Familiarity with eval and observability tooling (e.g., Braintrust, LangSmith, Arize, Weave, Promptfoo) or experience building custom eval harnesses
Experience with failure-mode analysis on agent or RAG systems – categorizing errors (hallucination, retrieval miss, planning failure, tool misuse) and driving each down with targeted evals
Hands-on experience with the MLOps lifecycle, including model deployment, monitoring, and evaluation in a cloud environment (AWS, Azure, or GCP)
Familiarity with core data science libraries and tools (e.g., pandas, numpy, Snowpark)
Startup experience or experience in a high-growth, fast-paced environment
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Demonstrated experience leading technical projects or teams, including setting technical direction, reviewing others' work, and driving delivery to completion
Proven experience building and productionizing applications using LLMs, especially with technologies like RAG and agentic workflows
Hands-on experience defining quality metrics and evaluation frameworks for LLM or agent systems, and using evals to systematically improve quality over time
Excellent problem-solving and communication skills, with an ability to articulate complex technical concepts to both technical and executive stakeholders
Comfort with ambiguity and the ability to independently structure and execute on complex, open-ended problems
5+ years of professional software engineering experience
Experience in a customer-facing technical role
Willingness to travel