Who We Are:

At the intersection of health plans and providers, Apixio is creating a leading Connected Care platform to minimize reimbursement inaccuracies and high-quality patient care so they can thrive as the industry moves toward value-based reimbursement models. 

The combination brings together healthcare expertise, AI/machine learning technology and data-driven analytics solutions to deliver innovative solutions and value to our customers and the healthcare ecosystem. We aim to accelerate the shift toward alternative payment models, while enhancing efficiency and supporting better patient outcomes.

About the role:

We are seeking a skilled MLOps Engineer with expertise in Spark, Python, GPU and preferably Databricks to join our team. As an MLOps Engineer, you will play a critical role in operationalizing and automating machine learning workflows, ensuring scalability, reliability, and efficiency. You will collaborate closely with data scientists, software engineers, and DevOps teams to deploy, monitor, and manage machine learning models in production environments.

Who you are: 

Daily responsibilities include Development and Management of key system areas including:

  • Design, implement, and maintain scalable MLOps infrastructure and pipelines using Apache Spark, Python, and other relevant technologies.
  • Collaborate with data scientists and software engineers to deploy machine learning models into production environments.
  • Develop and automate CI/CD pipelines for model training, testing, validation, and deployment.
  • Implement monitoring, logging, and alerting solutions to track model performance, data drift, and system health.
  • Optimize and tune machine learning workflows for performance, scalability, and cost efficiency.
  • Ensure security and compliance requirements are met throughout the MLOps lifecycle.
  • Work closely with DevOps teams to integrate machine learning systems with existing infrastructure and deployment processes.
  • Provide technical guidance and support to cross-functional teams on best practices for MLOps and model deployment.
  • Stay updated on emerging technologies, tools, and best practices in MLOps and machine learning engineering domains.
  • Perform troubleshooting and resolution of issues related to machine learning pipelines, infrastructure, and deployments.

What you bring to the table:

  • Bachelor's degree in Computer Science, Engineering, Mathematics, Statistics, or a related field.
  • Proven experience (5+ years) as a MLOps Engineer, Software engineer, DevOps Engineer or related role.
  • Excellent communication and collaboration skills, with the ability to work effectively in cross-functional teams.
  • Strong understanding of machine learning concepts, algorithms, and frameworks such as MLFlow, TensorFlow, PyTorch, or Scikit-learn.
  • Knowledge of big data processing technologies such as Apache Spark for handling large-scale data and distributed computing.
  • Experience with cloud platforms such as AWS, Azure, or Google Cloud Platform (GCP) and familiarity with services like AWS SageMaker, Azure Machine Learning, or Google AI Platform.
  • Understanding of containerization technologies like Docker and container orchestration tools like Kubernetes for managing machine learning workflows in production environments.
  • Proficiency in version control systems (e.g., Git) and CI/CD tools for automating the deployment and management of machine learning models.
  • Hands-on experience with Databricks for data engineering and analytics (nice to have).
  • Experience designing and implementing CI/CD pipelines for machine learning workflows using tools like Jenkins, GitLab CI, or Azure DevOps.
  • Knowledge of version control systems (e.g., Git) and collaborative development workflows.
  • Strong problem-solving skills and attention to detail, with the ability to troubleshoot complex issues in distributed systems.

Nice to Have

  • Masters degree in information technology, computer science, software engineering, or data science preferred
  • Healthcare Domain expertise
  • Experience productionizing large NLP models
The salary range below is for Base Salary.  Total compensation also includes benefits and variable compensation.  Compensation will be determined based on several factors including, but not limited to, skill set, years of experience, and the employee’s geographic location.
Base Compensation
$152,000—$228,000 USD

We recognize that people come with experience and talent beyond just the technical requirements of a job. If your experience is close to what you see listed here, please consider applying. Diversity of experience and skills combined with passion is a key to innovation and excellence. Therefore, we encourage people from all backgrounds to apply to our positions. Your skills and background may be more translatable to this role than you initially thought.  Allow us the opportunity to get to know you.  Please let us know if you require accommodations during the interview process.

What Apixio can offer you:

  • Meaningful work to advance healthcare 
  • Competitive compensation
  • Exceptional benefits, including medical, dental and vision, FSA/HSA
  • Generous vacation policy

We take your privacy very seriously. Please review our privacy policy to see exactly how we protect your information here

We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. We are proud to be an equal opportunity workplace.

 

Notice to Recruiters/Staffing Agencies

  • Recruiters and staffing agencies should not contact Apixio through this page. All recruitment vendors (search firms, recruitment agencies, and staffing companies) are prohibited from contacting our hiring manager(s), executive team members, or employees
  • We require that all recruiters and staffing agencies have a fully executed, formal written agreement on file
  • Apixio receipt or acceptance of an unsolicited resume submitted by a vendor organization to this website or employee does not constitute an actual or implied contract between Apixio and such organization and will be considered unsolicited and Apixio will not be responsible for related fees

 

 

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