AIML Engineer

At Commence were 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 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

• Bachelors 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 skills across technical and non-technical teams.

Preferred Qualifications

• Exposure to vector databases embeddings or RAG components.
• Experience with OCR/document AI tools (e.g. AWS Textract DBX OCR).
• Experience with containerization and orchestration (Docker Kubernetes).
• Familiarity with healthcare data governance and security frameworks such as NIST or HITRUST.
• Experience supporting federal healthcare programs or agencies such as CMS VA or DoD.
• Knowledge of healthcare delivery systems quality measurement programs or policy frameworks.

Work Environment/Physical Demands

The work environment and physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

This is a remote position. While performing the duties of this job the employee regularly works in a climate-controlled environment. Candidates must be able to sit read work on a computer and watch a computer screen for extended periods of time. Occasionally required to stand walk use hands and fingers kneel or crouch.

Commence is an equal employment opportunity employer. All personnel processes are merit-based and applied without discrimination on the basis of race color religion sex sexual orientation gender identity marital status age disability national or ethnic origin military and veteran status or any other characteristic protected by applicable law.

Commence is committed to providing equal employment opportunities to all applicants including individuals with disabilities. If you require a reasonable accommodation to participate in the application process due to a disability please contact Human Resources at or Please note that unless you are requesting an accommodation all applications must be submitted through our online application system.