Sr. AI Software Engineer

We are seeking a Senior AI Engineer to lead the design, development, and production deployment of advanced AI systems on Azure and Databricks, reporting to the Manager, Software Engineering & AI. You will own the technical direction for agentic AI platforms, Retrieval Augmented Generation (RAG) architectures, and large-scale model training and serving across the organization. This is a senior individual-contributor role with significant architectural authority: you will set standards, mentor engineers, partner closely with product and clinical stakeholders, and be accountable for the reliability, cost, and clinical safety of the AI systems you deliver. Success in this role requires deep machine learning expertise, proven production experience on Azure and Databricks, and strong judgment to make sound trade-offs in a regulated healthcare environment. 

Responsibilities:  

• Architect and lead end-to-end agentic AI systems enabling autonomous decision-making, multi-step planning, and tool use; design planning, reasoning, memory, and orchestration layers with strong evaluation, observability, and guardrails 

• Drive performance, safety, and cost optimization for agents operating in complex, dynamic environments 

• Own production RAG architecture across structured and unstructured oncology data; optimize for retrieval quality, latency, and grounding 

• Design scalable indexing, hybrid retrieval strategies, and evaluation frameworks; lead fine-tuning, adapters, and prompt engineering to improve LLM performance 

• Lead training and deployment of LLMs and deep learning models on Azure ML and Databricks, including distributed GPU training 

• Define scalable data preprocessing, feature engineering, and labeling pipelines; drive optimization across hyperparameters, architecture, quantization, and serving 

• Own full model lifecycle: versioning, evaluation, monitoring, drift detection, and retraining 

• Set technical direction for AI workloads across Azure and Databricks; design secure, scalable, HIPAA-compliant data and ML pipelines 

• Establish standards for cost, performance, reliability, and capacity planning, including FinOps for AI infrastructure 

• Define and enforce MLOps practices including CI/CD, automated evaluation, A/B testing, canary deployments, and incident response 

• Build reusable platform tooling to accelerate safe and efficient AI feature delivery 

• Mentor engineers, lead design reviews, and elevate code quality and evaluation rigor 

• Collaborate cross-functionally to deliver AI solutions aligned with clinical and business outcomes 

• Translate ambiguity into clear technical roadmaps and communicate trade-offs to executive, technical, and clinical stakeholders 

• Shape organizational