Lead Software Engineer

Key Responsibilities

• 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 & 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.
• Judgment about when a large language model is the right solution and when it is not.
• Cost consciousness — weighing model, token, and infrastructure cost as part of the design, and keeping it under control once a solution is running.
• Written and verbal communication skills – ability to present potential solutions, pros and cons to senior stakeholders.
• Collaborative mindset with ability to work directly with stakeholders across the business.
• Able to run several efforts at once without dropping quality.
• Project management: scoping, sequencing, estimating, and reporting progress against commitments.