Senior Machine Learning Engineer (Interwell Health)

End client: Interwell Health. Sourced via Red Hibbert Group. Rate: $70-75/hr on W2 (contract). Visa: open to all work-authorized candidates.

About the Role

Interwell Health is seeking a Senior Machine Learning Engineer to join its growing AI and Data Science team. This is an excellent opportunity for an experienced engineer who thrives in a fast-paced environment, enjoys taking ownership of complex machine learning initiatives, and has a strong background in building production-grade ML solutions within the healthcare industry.
The ideal candidate will have 8+ years of professional experience, including extensive expertise across the complete machine learning lifecycle-from data preparation and feature engineering to model deployment, monitoring, and optimization. Healthcare experience is mandatory, and candidates should have experience working with sensitive healthcare data while following HIPAA and other compliance standards.

Key Responsibilities

• Take complete ownership of designing, developing, deploying, and maintaining enterprise-scale Machine Learning solutions.
• Build end-to-end ML pipelines: data ingestion, feature engineering, model training, evaluation, deployment, monitoring, retraining.
• Design 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 model drift, data drift, accuracy degradation, and overall system health.
• Collaborate with Data Engineers, Software Engineers, Product Managers, Clinical teams, and business stakeholders.
• Develop REST APIs and integrate ML services into enterprise cloud applications.
• Optimize models for latency, scalability, reliability, and operational cost.
• Provide technical leadership on AI/ML initiatives.
• Ensure compliance with HIPAA, PHI, PII, and enterprise security standards.

Required Qualifications

• 8+ years overall software engineering and ML experience.
• Strong healthcare industry experience mandatory.
• Full ML lifecycle: data preprocessing, feature engineering, model development, model calibration, deployment, monitoring, maintenance.
• Strong ownership mindset; hands-on MLOps; strong Python + SQL.
• Distributed computing (Spark); Databricks in production; Azure/AWS/GCP.
• API development and integration; strong debugging/perf tuning; excellent communication.

Required Technical Skills: Python, SQL, Machine Learning, MLOps, Databricks, Apache Spark, MLflow, Feature Store, Model Registry, CI/CD Pipelines, Azure/AWS/GCP, REST APIs, Git, Docker, Kubernetes (preferred).

Preferred: LLMs in production, prompt engineering/RAG, GenAI, Scala, Azure ML/SageMaker/Vertex AI, HIPAA-compliant AI solutions, distributed ML architecture.