ML Engineer (US Remote)

About the Role

Jobot Consulting is seeking a Machine Learning Engineer, Consultant to support a healthcare-focused organization working extensively with Electronic Health Record (EHR) environments. This remote contract role focuses on production machine learning, scalable ML infrastructure, AI/GenAI model engineering, and MLOps. The ideal candidate has at least 3 years of relevant machine learning engineering experience, hands-on healthcare and EHR experience, and a strong background in deploying and maintaining production-grade ML systems. Experience with containerization technologies such as Docker or Kubernetes is highly relevant to the role.

Responsibilities

• Deploy and maintain production-grade machine learning models with a focus on real-time inference, scalability, and reliability.
• Develop end-to-end, scalable ML infrastructure across on-premises and cloud environments.
• Work with AWS, GCP, Azure, or similar cloud platforms to support machine learning infrastructure.
• Lead engineering efforts related to ML and GenAI model engineering, LLM advancements, and deployment framework optimization.
• Align ML engineering methods and workflows with business and technical objectives.
• Develop AI pipelines supporting data ingestion, preprocessing, search, and retrieval requirements.
• Ensure AI pipelines satisfy technical and business requirements.
• Collaborate with data scientists, data engineers, analytics teams, and DevOps teams to build robust deployment pipelines.
• Implement and optimize CI/CD pipelines for machine learning models.
• Automate testing and deployment processes for ML systems.
• Establish monitoring and logging solutions to track model performance, system health, and anomalies.
• Support proactive maintenance and timely intervention when issues are identified.
• Implement version control for machine learning models and associated code.
• Ensure ML systems follow applicable security, data protection, privacy, and compliance requirements.
• Maintain clear and comprehensive documentation of MLOps processes and configurations.
• Support integration of machine learning models with healthcare and EHR systems.

Required Qualifications

• 3+ years of relevant Machine Learning Engineer experience.
• Proven experience deploying and maintaining production-grade machine learning models.
• Experience with real-time ML inference, scalability, and system reliability.
• Experience developing scalable, end-to-end ML infrastructure.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Experience developing AI pipelines for data ingestion, preprocessing, search, and retrieval.
• Experience implementing CI/CD pipelines for machine learning systems.
• Experience with monitoring and logging for ML models and infrastructure.
• Experience with version control for ML models and associated code.
• Knowledge of security, data protection, privacy, and compliance requirements for machine learning systems.
• Strong documentation skills for MLOps processes and configurations.
• Healthcare industry experience is required.
• Electronic Health Record (EHR) experience is required.

Preferred Qualifications

• Experience with containerization technologies such as Docker or Kubernetes.
• Experience integrating machine learning models with EHR systems.
• Understanding of healthcare regulations and standards.
• Experience with ML/GenAI model engineering and LLM advancements.
• Experience optimizing ML deployment frameworks.
• Bachelor's degree in Computer Science, Artificial Intelligence, Informatics, or a closely related field.
• Master's degree.
• Machine Learning certification(s).

Skills & Competencies

• Machine Learning Engineering
• Production ML
• MLOps
• Generative AI
• LLM Engineering
• AI Pipeline Development
• Model Deployment
• Real-Time Inference
• ML Infrastructure
• AWS
• GCP
• Azure
• CI/CD
• Monitoring and Logging
• Version Control
• Docker
• Kubernetes
• Data Ingestion
• Data Preprocessing
• Search and Retrieval
• EHR Systems
• Healthcare Technology
• Security and Compliance
• Technical Documentation
• Cross-Functional Collaboration

Pay

$75–$150 per hour

Schedule

• Location: Bakersfield, CA, US
• Work Arrangement: Remote
• Time Zone: Pacific Standard Time (PST) hours
• Contract Duration: 12+ months
• Employment Options: W2 or C2C
• Employment Type: Contract
• Industry Focus: Healthcare / Electronic Health Records