Note: The job is a remote job and is open to candidates in USA. Commence develops data-centric, healthcare-focused solutions that use technology, clinical expertise, and data to improve care quality and operational efficiency. The AI/ML Engineer builds, validates, and productionizes machine learning models and AI/LLM workflows for clinical and operational data products while supporting MLOps, monitoring, compliance, and collaboration with technical teams.
Responsibilities
- 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
Skills
- 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 skills across technical and non-technical teams
- Candidates must be able to sit, read, work on a computer, and watch a computer screen for extended periods. Occasionally required to stand, walk, use hands and fingers, kneel or crouch
- 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
Benefits
- This is a remote position.
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