Lead AI Software Engineer

Role Overview

Design and deliver AI/ML solutions for the healthcare industry, spanning LLM-powered applications, retrieval-augmented generation (RAG), and predictive models. Co-design AI solutions with the AI Architect and own product-level design and end-to-end implementation within enterprise standards.

What You Will Do

Lead end-to-end implementation for your product/squad, including RAG over EHR/claims/clinical text using embeddings and vector search, model development and training/fine-tuning, production deployment, and establishing and operating MLOps.

Why It Might Be a Fit

Healthcare domain experience, either clinical or revenue cycle management, is required. Strong competence with SQL Server and Visual Studio is essential. A commitment to HIPAA-compliant handling of PHI, Responsible AI, and measurable clinical and business outcomes is essential.

Requirements

• Bachelor’s degree in Computer Science, Information Technology, Computer Information Systems, Health Informatics, or a related field
• Healthcare domain experience: either clinical (e.g., care delivery, clinical documentation, quality) or revenue cycle management (e.g., coding, claims, denials, prior auth)
• 10+ years in software engineering; 5+ years building and shipping ML/AI solutions; 2+ years leading AI/ML initiatives or teams
• Azure AI Foundry (Azure AI Studio) knowledge for developing, evaluating, and operationalizing LLM solutions
• Proficiency with SQL Server Management Studio (SSMS) for SQL development, performance tuning, and troubleshooting
• Proficiency with Visual Studio and experience integrating AI services into.NET/C# applications or services where needed
• Hands-on experience using OpenAI or Anthropic models (e.g., GPT-4.x/4o, Claude 3.x)
• Experience with Microsoft Copilot and GitHub Copilot in professional workflows
• Strong Python and ML ecosystem: PyTorch/TensorFlow, transformers/Hugging Face, embeddings, LLM orchestration (e.g., LangChain or LlamaIndex), and vector databases (e.g., FAISS, Azure AI Search, Pinecone)
• MLOps: MLflow/W&B (or equivalent), Docker, Kubernetes, CI/CD for ML, model registries, monitoring (drift, performance, bias), A/B testing, and rollback strategies
• Cloud AI on Azure (preferred): Azure AI Foundry, Azure OpenAI, Azure AI Search, Azure ML, Azure Key Vault, with solid understanding of IAM, secrets, and encryption
• Demonstrated security, privacy, and compliance competence: HIPAA, PHI/PII handling and de-identification (e.g., Presidio), Responsible AI practices

Benefits

• Competitive compensation and benefits packages
• Flexible, work environment conducive to collaboration and productivity
• Opportunities for growth and continued learning
• Valued and recognized individual contribution