Hands-on experience with embedding models and vector databases.
Hands-on experience building agentic workflows and implementing EvalOps or Evals-as-a-Service architecture.
Deep familiarity with the LLM ecosystem, able to critically assess model capabilities, limitations, and fit, including heuristic-gated model routing and cost, quality, speed, and capability tradeoffs.
Proven experience designing evaluation frameworks for LLM output quality in high-stakes domains (legal, medical, or similar), including hallucination detection.
Comfort in a fast-paced, high-ambiguity environment, with strong ownership and a bias for systematic process-building over one-off fixes.
Excellent communication skills; able to translate complex model evaluation findings into clear recommendations for technical and non-technical stakeholders.
Bonus: experience with unstructured medical or legal document processing, or a background in classical ML (statistics, embeddings, RAG).
Responsibilities
Hands-on development of multiple RAG solutions.
Hands-on experience with embedding models and vector databases.
Hands-on experience building agentic workflows and implementing EvalOps or Evals-as-a-Service architecture.
Deep familiarity with the LLM ecosystem, able to critically assess model capabilities, limitations, and fit, including heuristic-gated model routing and cost, quality, speed, and capability tradeoffs.
Proven experience designing evaluation frameworks for LLM output quality in high-stakes domains (legal, medical, or similar), including hallucination detection.
Comfort in a fast-paced, high-ambiguity environment, with strong ownership and a bias for systematic process-building over one-off fixes.
Excellent communication skills; able to translate complex model evaluation findings into clear recommendations for technical and non-technical stakeholders.
Bonus: experience with unstructured medical or legal document processing, or a background in classical ML (statistics, embeddings, RAG).
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