Sr. AI/ML Engineer

Commence•Virginia Beach, VA

About The Position

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. You will partner 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 requirements.

Requirements

Bachelor’s degree in Computer Science, Engineering, or a related field. Master’s degree preferred.
6+ years of experience in machine learning engineering, software engineering, or related roles.
Strong proficiency in programming languages such as Python and SQL, with experience building production-grade systems.
Demonstrated experience deploying and managing machine learning models in production environments.
Experience implementing AI/LLM solutions using platforms such as AWS Bedrock, Anthropic, LangChain, Databricks Agent frameworks, or similar.
Experience 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.
Strong understanding of software engineering principles, system design, and scalable architecture.
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.

Nice To Haves

Experience with vector databases, embeddings, and RAG architectures.
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.

Responsibilities

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 requirements and deliver scalable AI solutions.
Ensure AI/ML systems comply 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.
Evaluate and adopt emerging AI/ML technologies, tools, and frameworks to improve system capabilities and performance.
Mentor junior and mid-level engineers in best practices related to ML engineering, system design, and production AI systems.