AI/ML Engineer
About The Position
At Commence, the AI/ML Engineer builds, trains, and validates machine learning models that power clinical and operational data products across the healthcare ecosystem. The role contributes hands-on to productionizing models, supporting document-processing pipelines and AI/LLM-driven workflows. The ideal candidate has practical machine learning experience, is comfortable working with healthcare datasets such as EHRs, claims, and FHIR, and can independently own well-scoped model development work while relying on senior engineers for system design, compliance, and architecture decisions. The engineer contributes to model development and deployment across APIs, batch, and streaming workflows; supports AI/LLM and RAG components within established technical frameworks; applies existing MLOps practices; and uses Databricks and AWS services for training and inference. The role also monitors model performance, troubleshoots issues, collaborates across technical disciplines, follows security and regulatory practices for sensitive healthcare data, stays current with relevant tools, and documents work for reproducibility and audit readiness. This is a remote position. The work is generally performed in a climate-controlled environment and involves extended periods sitting, reading, working at a computer, and watching a computer screen. Occasional standing, walking, hand and finger use, kneeling, or crouching may be required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.
Requirements
- 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 machine learning 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.
Nice To Haves
- 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.
Responsibilities
- Build, train, and validate machine learning models, including data ingestion, feature engineering, and 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 AI/LLM-driven workflows, such as document extraction, classification, and 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 architectures designed by senior engineers.
- Apply established MLOps practices, including CI/CD pipelines, model versioning, and retraining workflows, within existing team standards.
- Use Databricks (Workflows, Delta Lake, Unity Catalog) and AWS services (S3, Lambda, Bedrock) to support model training and inference tasks.
- Monitor deployed model performance, including latency, throughput, and accuracy drift; troubleshoot issues and escalate 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.
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What This Job Offers
Job Type
Full-time
Career Level
Mid Level
