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)
Experience in a customer-facing technical role (e.g., solutions architect, sales engineer, or professional services)
Startup experience
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Bachelor's degree in Computer Science, Engineering, a related technical field, or equivalent practical experience
3+ years of professional software engineering experience
Willingness to travel
Proven experience building applications using LLMs, especially with technologies like RAG and agentic workflows
Hands-on experience defining quality metrics and running evaluations for LLM or agent systems, and using evals to systematically improve quality
Excellent problem-solving and communication skills, with an ability to articulate complex technical concepts to diverse stakeholders
Comfort with ambiguity and a desire to thrive in a fast-paced, ever-changing Generative AI environment