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Machine Learning Engineer
Machine Learning Engineerjobbot • Denver, CO
Machine Learning Engineer

Machine Learning Engineer

jobbot • Denver, CO
[job_card.variable_days_ago]
[job_preview.job_type]
  • [job_card.full_time]
[job_card.job_description]

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!

Apply Today!

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 (, AWS, Azure or GCP). Containerization technologies (, Docker) or container orchestration platforms (, Kubernetes). CI / CD tools (, Github Actions). Programming languages and frameworks (, 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 (, Docker) or container orchestration platforms (, Kubernetes).
  • CI / CD tools (, Github Actions).
  • Programming languages and frameworks (, 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 (GCP), 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.

    • Jobot is an Equal Opportunity Employer. We provide an inclusive work environment that celebrates diversity and all qualified candidates receive consideration for employment without regard to race, color, sex, sexual orientation, gender identity, religion, national origin, age (40 and over), disability, military status, genetic information or any other basis protected by applicable federal, state, or local laws. Jobot also prohibits harassment of applicants or employees based on any of these protected categories. It is Jobot’s policy to comply with all applicable federal, state and local laws respecting consideration of unemployment status in making hiring decisions.

    Sometimes Jobot is required to perform background checks with your authorization. Jobot will consider qualified candidates with criminal histories in a manner consistent with any applicable federal, state, or local law regarding criminal backgrounds, including but not limited to the Los Angeles Fair Chance Initiative for Hiring and the San Francisco Fair Chance Ordinance.

    Information collected and processed as part of your Jobot candidate profile, and any job applications, resumes, or other information you choose to submit is subject to Jobot's Privacy Policy, as well as the Jobot California Worker Privacy Notice and Jobot Notice Regarding Automated Employment Decision Tools which are available at

    By applying for this job, you agree to receive calls, AI-generated calls, text messages, or emails from Jobot, and / or its agents and contracted partners. Frequency varies for text messages. Message and data rates may apply. Carriers are not liable for delayed or undelivered messages. You can reply STOP to cancel and HELP for help. You can access our privacy policy here :

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