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.
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