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 learningexpertise, 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 agentsoperatingin complex, dynamic environments

• Own production RAG architecture across structured and unstructured oncology data;optimizefor 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 enforceMLOpspractices 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 AI strategy, standards, and responsible AI practices

• Evaluate emerging models and techniques, driving adoption where valuable

• Represent the organization in industry forums, conferences, and partner engagements

• Additionalresponsibilitiesasassigned to help drive our mission of improving the lives of everyone living with cancer.

Requiredor PreferredQualifications(specify within)

• Bachelor’s orMaster’s in Computer Science, AI, or related field (or equivalent experience)

• 7+ years software/ML engineering, including 3+ years building and operating production ML/LLM systems

• Proventrack recorddelivering AI/ML systems from prototype to scaled production

• Deepexpertisein LLMs, transformers, fine-tuning, and inference optimization

• Expert Python; strong foundations in distributed systems, data engineering, and software design

• Required experience with Azure and Databricks (Azure ML, Databricks workflows,MLflow, Spark pipelines)

• Hands-on experience with vector databases (e.g., Azure AI Search, Databricks Vector Search,pgvector, FAISS) and RAG patterns

• Experience with LLM orchestration and agent frameworks (e.g.,LangChain,LlamaIndex, Semantic Kernel)

• Strong evaluation practices (offline/online evals, golden datasets, quality metrics for LLM/agent systems)

• Excellent problem-solving, communication, and cross-functional collaboration skills

Preferred:

• Experience designing and shipping agentic AI systems in production.

• Experience implementing RAG over regulated or sensitive data (healthcare, finance, legal).

• Experience withMLOpstooling and platform engineering for AI (model registries, feature stores, serving infrastructure, observability).

• Experience with healthcare data standards (FHIR, HL7) andoperatingwithin HIPAA-compliant environments.

• Experience mentoring engineers and leading technical initiatives across teams.

• Contributions to open-source AI projects, publications, or patents.