Senior Machine Learning Engineer

A growing AI and Data Science team at a healthcare-focused company is looking for a Senior Machine Learning Engineer to take ownership of complex, enterprise-scale ML initiatives. This is a W2 contract role for work-authorized candidates (no visa sponsorship available). You’ll work in a fast-paced environment building production-grade ML solutions that directly impact patient outcomes and healthcare operations - with a strong emphasis on compliance, reliability, and end-to-end ownership., * Own the full ML lifecycle: data ingestion, feature engineering, model training, evaluation, deployment, monitoring, retraining, and maintenance.

• Design and build scalable, production-ready ML systems with high availability, performance, and reliability.
• Develop and maintain MLOps pipelines - including CI/CD, model registry, feature stores, automated deployment, monitoring, and rollback strategies.
• Monitor production models for drift (model, data, accuracy degradation) and overall system health.
• Build and integrate REST APIs to connect ML services into enterprise cloud applications.
• Optimize models for latency, scalability, reliability, and operational cost.
• Provide technical leadership on AI/ML initiatives across the organization.
• Collaborate with Data Engineers, Software Engineers, Product Managers, Clinical teams, and business stakeholders.
• Ensure strict compliance with HIPAA, PHI, PII, and enterprise security standards throughout all ML workflows.

Ideal candidates bring 8+ years of professional ML engineering experience and a mandatory background in the healthcare industry, including hands-on experience with HIPAA-compliant systems and sensitive patient data., Required - Dealbreakers:

• 8+ years of professional software engineering and machine learning experience.
• Healthcare domain experience is mandatory - including HIPAA compliance and handling of sensitive patient data (PHI/PII).
• Demonstrated ownership of end-to-end ML lifecycle from data preparation through deployment, monitoring, and retraining.
• Experience designing and operating production-grade ML systems at scale.
• Hands-on MLOps: CI/CD pipelines, model registry, feature stores, automated deployment, monitoring, and rollback.

Required Technical Skills:

• Languages: Python, SQL
• Platforms: Databricks (production), Apache Spark (distributed computing), MLflow, Feature Store, Model Registry
• Cloud: Azure, AWS, and/or GCP for ML workloads
• Infrastructure: Docker, Kubernetes, REST APIs, Git, CI/CD pipelines
• Strong debugging and performance-tuning skills; excellent stakeholder communication.

Nice to Have:

• LLMs in production, prompt engineering, RAG, and/or GenAI applications
• Scala
• Azure ML, SageMaker, or Vertex AI
• Distributed ML architecture design
• HIPAA-compliant AI solution design experience