Jobs at LawPro.ai

Own evaluation, selection, and continuous optimization of LLMs powering the analytics platform. Build Eval frameworks, benchmark and migrate models, implement RAG and embedding-based pipelines, optimize cost/performance, deploy model changes to cloud production, monitor performance and drift, and document methodologies and runbooks. Collaborate with product and operations and take end-to-end ownership of AI engineering work.

Own evaluation, selection, and continuous optimization of LLMs and AI pipelines for legal and medical document tasks. Build Eval frameworks, implement RAG and embedding-based systems, integrate and transition models into production on cloud, monitor performance and cost, and document runbooks. Collaborate cross-functionally and take end-to-end ownership of deployments and validation.

Own evaluation, selection, and production optimization of LLMs and AI pipelines for legal and medical document tasks. Design Eval frameworks, benchmark models for accuracy, cost, and speed, implement model transitions, build and deploy RAG and embedding-based solutions on cloud, monitor production performance, and maintain documentation and runbooks with end-to-end ownership.

Own evaluation, selection, and continuous optimization of LLMs and AI processes powering the analytics platform. Build EvalOps frameworks, benchmark and transition models, implement RAG/embedding/vector DB solutions, optimize orchestration pipelines, deploy model changes end-to-end, monitor performance/cost/drift, and document runbooks. Collaborate cross-functionally and execute production deployments.

Own evaluation, selection, and production integration of LLMs. Build EvalOps frameworks, benchmark models, optimize RAG and orchestration pipelines, deploy model changes to cloud, implement monitoring, and document runbooks while collaborating with product and operations.

Own evaluation, selection, and continuous optimization of LLMs and AI pipelines. Build EvalOps frameworks, benchmark models, implement model transitions, optimize RAG/document understanding pipelines, deploy and validate production changes, monitor performance/cost, and document runbooks.