Machine Learning Engineer, Remote Consultant

This Jobot Consulting Job is hosted by: Ashley Elm
Are you a fit? Easy Apply now by clicking the "Apply Now" button and sending us your resume.
Salary: $75 - $94 per hour

A bit about us:

Leading west coast based healthcare system is looking to add a remote Machine Learning Engineering consultant to their team.
Apply today to learn more about this 12+ month consulting role opportunity.
To be considered, candidates must reside in PST, MTN, or CST time zones.

*Please note you must have EHR and Healthcare setting experience to be considered.

Why join us?

12+ Month REMOTE Contract role with options to extend
PST Hours

Job Details

Requirements:

• 3 or more years relevant Machine Learning Engineer Experience
• Bachelor's Degree computer science, artificial intelligence, informatics or closely related field, Masters preferred
• Healthcare Expertise: Understanding of healthcare regulations and standards, and familiarity with Electronic Health Records (EHR) systems, including integrating machine learning models with these systems.
• Certification(s) in Machine Learning a plus
• Experience in managing end-to-end ML lifecycle.
• Experience in managing automation with Terraform.
• Containerization technologies (e.g., Docker) or container orchestration platforms (e.g., Kubernetes).
• CI/CD tools (e.g., Github Actions).
• Programming languages and frameworks (e.g., Python, R, SQL).
• Deep understanding of coding, architecture, and deployment processes.
• Strong understanding of critical performance metrics.
• Extensive experience in predictive modeling, LLMs, and NLP.

? Exhibit the ability to effectively articulate the advantages and applications of the RAG framework with LLMs

Job Description:

• Production Deployment and Model Engineering: Proven experience in deploying and maintaining production-grade machine learning models, with real-time inference, scalability, and reliability.
• Scalable ML Infrastructures: Proficiency in developing end-to-end scalable ML infrastructures using on-premise cloud platforms such as Amazon Web Services (AWS), Google Cloud Platform (Google Cloud Platform), or Azure.
• Engineering Leadership: Ability to lead engineering efforts in creating and implementing methods and workflows for ML/GenAI model engineering, LLM advancements, and optimizing deployment frameworks while aligning with business strategic directions.
• AI Pipeline Development: Experience in developing AI pipelines for various data processing needs, including data ingestion, preprocessing, and search and retrieval, ensuring solutions meet all technical and business requirements.
• Collaboration: Demonstrated ability to collaborate with data scientists, data engineers, analytics teams, and DevOps teams to design and implement robust deployment pipelines for continuous improvement of machine learning models.
• Continuous Integration/Continuous Deployment (CI/CD) Pipelines: Expertise in implementing and optimizing CI/CD pipelines for machine learning models, automating testing and deployment processes.
• Monitoring and Logging: Competence in setting up monitoring and logging solutions to track model performance, system health, and anomalies, allowing for timely intervention and proactive maintenance.
• Version Control: Experience implementing version control systems for machine learning models and associated code to track changes and facilitate collaboration.
• Security and Compliance: Knowledge of ensuring machine learning systems meet security and compliance standards, including data protection and privacy regulations.
• Documentation: Skill in maintaining clear and comprehensive documentation of ML Ops processes and configurations.

Interested in hearing more? Easy Apply now by clicking the "Apply Now" button.

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