Senior Machine Learning Engineer

About the Role

We are an IT services consultancy placing a Senior Machine Learning Engineer with one of our end clients — a growing healthcare technology organization focused on AI and Data Science. This is a W2 contract engagement ideal for an experienced ML engineer who thrives in fast-paced environments, takes strong ownership of complex initiatives, and has a proven track record building production-grade ML solutions within the healthcare industry.

You will join the client's AI and Data Science team and lead end-to-end machine learning efforts spanning the full model lifecycle — from data preparation and feature engineering through to deployment, monitoring, and optimization — all within a HIPAA-compliant, enterprise-scale environment.

What You'll Do

• Take complete ownership of designing, developing, deploying, and maintaining enterprise-scale machine learning solutions.

• Build end-to-end ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, monitoring, and retraining.

• Design scalable, production-ready ML systems with a focus on 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 cross-functionally 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 across the team.

• Ensure compliance with HIPAA, PHI, PII, and enterprise security standards at all stages of development.

What We're Looking For

Required Qualifications

• 8+ years of professional software engineering and machine learning experience.

• Strong healthcare industry experience is mandatory; demonstrated ability to work with sensitive healthcare data under HIPAA and related compliance frameworks.

• Full ML lifecycle expertise: data preprocessing, feature engineering, model development, calibration, deployment, monitoring, and maintenance.

• Hands-on MLOps experience with a strong ownership mindset.

• Proficiency in Python and SQL.

• Experience with distributed computing (Apache Spark) and Databricks in production environments.

• Practical experience with major cloud platforms: Azure, AWS, and/or GCP.

• API development and integration skills; strong debugging and performance-tuning capabilities.

• Excellent communication skills for collaborating with technical and non-technical stakeholders.

Required Technical Skills

• Python, SQL, Machine Learning, MLOps

• Databricks, Apache Spark, MLflow

• Feature Store, Model Registry

• CI/CD Pipelines, REST APIs

• Git, Docker; Kubernetes (preferred)

• Azure / AWS / GCP

Preferred / Nice-to-Have

• LLMs in production; prompt engineering, RAG, and GenAI experience.

• Scala proficiency.

• Managed ML platform experience: Azure ML, Amazon SageMaker, and/or Google Vertex AI.

• Experience designing HIPAA-compliant AI solutions and distributed ML architectures.

Compensation & Benefits

• Rate: $70–75/hr on W2 (contract engagement).

• Visa Sponsorship: Not available — US work authorization required.

Location

Based in Palo Alto, CA. On-site / hybrid arrangement at the client's location.