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.?

Requirements The AI/ML Engineer builds, trains, and validates machine learning models that power clinical and operational data products across the healthcare ecosystem, and contributes hands-on to productionizing those models, including supporting document processing pipelines and AI/LLM-driven workflows.

The ideal candidate has solid hands-on ML experience, is comfortable working with healthcare datasets (EHRs, claims, FHIR), and can independently own well-scoped model development work while relying on senior engineers for system design, compliance, and architecture decisions.

• Build, train, and validate machine learning models, including data ingestion, feature engineering, and model evaluation, against requirements defined by Senior Engineers or Data Scientists.
• Contribute to productionizing machine learning and AI models into APIs, batch, and streaming workflows, under the direction of Senior AI/ML Engineers.
• Support development of AI/LLM-driven workflows (e.g., document extraction, classification, summarization) using frameworks and orchestration patterns established by senior technical staff.
• Assist in implementing Retrieval-Augmented Generation (RAG) components, embedding strategies, and vector-based retrieval within architecture designed by senior engineers.
• Apply established MLOps practices, including CI/CD pipelines, model versioning, and retraining workflows, within existing team standards.
• Work within Databricks (Workflows, Delta Lake, Unity Catalog) and AWS services (S3, Lambda, Bedrock) to support model training and inference tasks.
• Monitor deployed model performance (latency, throughput, accuracy drift) and troubleshoot issues, escalating architecture-level problems to Senior Engineers.
• Use and maintain existing monitoring, alerting, and observability tooling for models the engineer owns.
• Collaborate with Data Engineers, Data Scientists, and Software Engineers to deliver assigned components of larger AI/ML systems.
• Follow established practices to keep AI/ML work compliant with HIPAA, 42 CFR Part 2, FedRAMP Moderate, and other applicable regulatory frameworks.
• Implement secure data handling practices, including encryption, access controls, and protection of sensitive healthcare data.
• Stay current on emerging AI/ML tools and techniques and bring relevant options to the team for evaluation.
• Document model development, testing, and validation work to support reproducibility and audit readiness.

Qualifications

• Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field.
• 3-5 years of experience in machine learning engineering, data science, or software engineering.
• Strong proficiency in programming languages such as Python and SQL, with experience building production-grade systems.
• Practical experience with ML libraries such as Scikit-learn, TensorFlow, or PyTorch.
• Experience deploying machine learning models to production environments (batch or API-based), with support from senior engineers on system design.
• Exposure to AI/LLM platforms such as AWS Bedrock, Anthropic, LangChain, or Databricks Agent frameworks.
• Familiarity with distributed data processing frameworks such as PySpark, Databricks, or AWS EMR.
• Experience with ML lifecycle tools such as MLflow, model registries, and monitoring frameworks.
• Experience building APIs or services (e.g., FastAPI, Flask) for model inference.
• Solid understanding of software engineering principles and their application to ML workflows.
• Experience working with healthcare datasets such as EHRs, claims, FHIR, or HL7.
• Strong problem-solving skills and attention to detail.
• Strong communication and collaboration skil…