Sr. AI/ML Engineer

Description At Commence, we’re the start of a new age of data-centric transformation, elevating health outcomes and powering better, more efficient process to program and patient health. We combine quality data-driven solutions that fuel answers, technology that advances performance, and clinical expertise that builds trust to create a more efficient path to quality care. With human-centered, healthcare-relevant, and value-based solutions, we create new possibilities with data. We provide proof beyond the concept and performance beyond the scope with a focus on efficiencies that transform the lives of those we serve. With a culture driven by purpose, straightforward communication and clinical domain expertise, Commence cuts straight to better care.?

The Senior AI/ML Engineer is responsible for designing, building, and operationalizing scalable machine learning and AI systems that power clinical and operational data products across the healthcare ecosystem. This role focuses on productionizing AI/ML capabilities, including large language models (LLMs), document processing pipelines, and predictive systems within a secure and compliant environment. The Senior AI/ML Engineer lives in the Data & Analytics team and partners closely with Data Scientists, Data Engineers, and Product teams to transform models and prototypes into reliable, performant, and production-grade systems. The ideal candidate has strong data science and MLOps experience, is comfortable working with complex healthcare datasets (EHRs, claims, FHIR), and can build scalable AI solutions that meet regulatory and operational

. • Design, build, and maintain end-to-end machine learning pipelines, including data ingestion, feature engineering, model training, validation, deployment, and monitoring. • Productionize machine learning and AI models developed by Data Scientists into scalable APIs, batch, and streaming workflows. • Develop and optimize AI/LLM-driven workflows (e.g., document extraction, classification, summarization) using modern frameworks and orchestration patterns. • Implement Retrieval-Augmented Generation (RAG) pipelines, embedding strategies, and vector-based retrieval systems. • Establish and maintain MLOps practices, including CI/CD pipelines, model versioning, lineage tracking, and automated retraining workflows. • Integrate AI/ML systems within Databricks (Workflows, Delta Lake, Unity Catalog) and AWS services (S3, Lambda, Bedrock). • Optimize model inference performance, latency, throughput, and cost efficiency across production systems. • Design and implement monitoring, alerting, and observability frameworks for deployed AI/ML systems. • Collaborate with Data Engineers, Data Scientists, and Software Engineers to define system