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
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Take complete ownership of designing, developing, deploying, and maintaining enterprise-scale machine learning solutions.
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Build end-to-end ML pipelines covering data ingestion, feature engineering, model training, evaluation, deployment, monitoring, and retraining.
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Design scalable, production-ready ML systems with a focus on high availability, performance, and reliability.
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Develop and maintain MLOps pipelines including CI/CD, model registry, feature stores, automated deployment, monitoring, and rollback strategies.
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Monitor production models for model drift, data drift, accuracy degradation, and overall system health.
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Collaborate cross-functionally with Data Engineers, Software Engineers, Product Managers, Clinical teams, and business stakeholders.
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Develop REST APIs and integrate ML services into enterprise cloud applications.
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Optimize models for latency, scalability, reliability, and operational cost.
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Provide technical leadership on AI/ML initiatives across the team.
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Ensure compliance with HIPAA, PHI, PII, and enterprise security standards at all stages of development.
What We're Looking For
Required Qualifications
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8+ years of professional software engineering and machine learning experience.
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Strong healthcare industry experience is mandatory ; demonstrated ability to work with sensitive healthcare data under HIPAA and related compliance frameworks.
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Full ML lifecycle expertise: data preprocessing, feature engineering, model development, calibration, deployment, monitoring, and maintenance.
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Hands-on MLOps experience with a strong ownership mindset.
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Proficiency in Python and SQL .
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Experience with distributed computing ( Apache Spark ) and Databricks in production environments.
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Practical experience with major cloud platforms: Azure, AWS, and/or GCP .
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API development and integration skills; strong debugging and performance-tuning capabilities.
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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
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LLMs in production; prompt engineering, RAG, and GenAI experience.
- Scala proficiency.
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Managed ML platform experience: Azure ML, Amazon SageMaker, and/or Google Vertex AI.
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Experience designing HIPAA-compliant AI solutions and distributed ML architectures.
Compensation & Benefits
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Rate: $70–75/hr on W2 (contract engagement).
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Visa Sponsorship: Not available — US work authorization required.
Location
Based in Palo Alto, CA . On-site / hybrid arrangement at the client's location.
Originally posted on Himalayas