Machine Learning Engineer

Machine Learning Engineer - Fulltime/Permanent REMOTE position

This Jobot Job is hosted by: Dallas Gillespie
Are you a fit? Easy Apply now by clicking the "Apply Now" button and sending us your resume.
Salary: $150,000 - $180,000 per year

A bit about us:

Full-time, fully remote position with excellent benefits
Candidates must live in the PST or MDT time zones. CST zone candidates will be considered. EST Candidates are not eligible.

Why join us?

We value you! Our impressive benefits package, bonus potential, and salary reflect that! Work somewhere you are recognized for your contributions!
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Job Details

Minimum Education:

• Bachelor's degree in computer science, artificial intelligence, informatics or closely related field.
• Master's degree in computer science, engineering or closely related field preferred.

Minimum Experience:

• 3 or more years of relevant Machine Learning Engineer Experience.
• Proven experience with: Artificial intelligence and machine learning platforms (e.g., AWS, Azure or Google Cloud Platform). 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). MLOps engineering principles, agile methodologies, and DevOps life-cycle management. Technical writing and documentation for AI/ML models and processes. Healthcare data and machine learning use cases.
• Healthcare Expertise: Understanding of healthcare regulations and standards, and familiarity with Electronic Health Records (EHR) systems, including integrating machine learning models with these systems.

REQUIRED qualifications:

• 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

Accountabilities:

• 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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