Collaborate with functional teams across any area of the business to understand their requirements, define the problem to be solved, and co-develop the solution.
Assess where a large language model is the right tool and where conventional software or a data solution serves the business better.
Design and build retrieval-augmented generation (RAG) solutions, including embedding, vector search, and orchestration, and select the models and services behind them.
Build evaluation and monitoring that gates each release and tracks quality and cost once solutions are in service.
Design for auditability, working with quality and regulatory stakeholders so that solutions hold up under inspection.
Build the integrations and data pipelines that these solutions depend upon, working against enterprise systems of record.
Own cloud architecture, cost management, security, and access control on Azure.
Partner with the enterprise applications team so that these solutions reach the people who need them.
Skills Knowledge and Expertise
Bachelor's degree in Computer Science, Engineering, a quantitative field, or equivalent practical experience.
5+ years building and shipping production software.
Experience delivering production applications built on large language models, including retrieval-augmented generation (RAG), prompt and context design, and evaluation.
Experience integrating with enterprise systems of record and working with the data they hold.
Experience building and deploying machine learning models on proprietary data. Preferred.
Experience in a regulated industry such as medical device or life sciences. Preferred.
Experience with document-heavy and unstructured data problems, including extraction, classification, and coding. Preferred.
Python, Node.js, and TypeScript.
Hands-on experience with vector databases and embedding models.
Hands-on experience on Azure, including Azure AI Foundry, along with data services, compute, identity, and networking.
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