ML Ops Engineer

Your Future Evolves Here

Evolent partners with health plans and providers to achieve better outcomes for people with most complex and costly health conditions. Working across specialties and primary care, we seek to connect the pieces of fragmented health care system and ensure people get the same level of care and compassion we would want for our loved ones.

Evolent employees enjoy work/life balance, the flexibility to suit their work to their lives, and autonomy they need to get things done. We believe that people do their best work when they're supported to live their best lives, and when they feel welcome to bring their whole selves to work. That's one reason why diversity and inclusion are core to our business.

Join Evolent for the mission. Stay for the culture.

What You’ll Be Doing:

MLOps Engineer

We are seeking a highly capable MLOps Engineer to join our growing AI/ML Team.  You will bridge the gap between data science and operations, ensuring that machine learning models are efficiently tested, deployed, monitored, and maintained in production environments.  You will work closely with data scientists, software engineers, infrastructure, and development teams to build scalable and reliable ML infrastructure.  You will be instrumental in supporting clinical decision-making, operational efficiency, quality outcomes, and patient care. 

What You Will Be Doing: 

Model Deployment and Infrastructure

• Design, build, and maintain scalable, secure ML pipelines for model training, validation, deployment, and monitoring
• Automate deployment workflows using CI/CD pipelines and infrastructure-as-code tools
• Partner with Infrastructure Teams to manage (Azure) cloud-based ML infrastructure, ensuring compliance with InfoSec and AI policies
• Ensure applications run at peak efficiency

Model Testing, Monitoring, and Validation

• Develop rigorous testing frameworks for ML models, including clinical validation, traditional model performance measures, population segmentation, and edge-case analysis
• Build monitoring systems to detect model drift, overfitting, data anomalies, and performance degradation in real-time
• Continuously analyze model performance metrics and operational logs to identify improvement opportunities
• Translate monitoring insights into actionable recommendations for data scientists to improve model precision, recall, fairness, and efficiency

Model Transparency & Governance

• Maintain detailed audit trails, logs, and metadata for all model versions, training datasets, and configurations to ensure full traceability and support internal audits
• Ensure models meet transparency and explainability standards using tools like SHAP, LIME, or integrated explainability APIs.
• Collaborate with data scientists and clinical teams to ensure models are interpretable, actionable, and aligned with practical applications
• Support corporate Compliance and AI Governance policies
• Advocate for best practices in ML engineering, including reproducibility, version control, and ethical AI
• Develop product guides, model documentation, and model cards for internal and external stakeholders

Required Qualifications:  

• Bachelor’s Degree in Computer Science, Machine Learning, Data Science, or a related field
• 2+ years of experience in MLOps, DevOps, or ML engineering

• Proficiency in Python and ML frameworks such as Keras, PyTorch, Scikit-Learn, TensorFlow, and XGBoost
• Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD tools
• Familiarity with healthcare datasets and privacy regulations
• Strong analytical skills to interpret model performance data and identify optimization opportunities
• Proven ability to optimize application performance, including improving code efficiency, right-sizing infrastructure usage, and reducing system latency
• Experience implementing rollback strategies, including version control, rollback triggers, and safe deployment practices across lower and upper environments
• 2+ years of experience developing in a cloud environment (AWS, GCS, Azure)
• 2+ years of experience with Github, Github Actions, CI/CD, and source control
• 2+ years working within an Agile environment

Preferred Qualifications:  

• Experience with MLOps platforms like MLflow, TFX, or Kubeflow
• Healthcare experience, particularly using administrative and prior authorization data
• Proven experience with developing and deploying ML systems into production environments 
• Experience working with Product, Engineering, Infrastructure, and Architecture teams
• Proficiency using Azure cloud-based services and infrastructure such as Azure MLOps
• Experience with feature flagging tools and strategies

To comply with HIPAA security standards (45 C.F.R. sec. 164.308 (a) (3)), identity verification may be required as part of the application process. This is collected for compliance and security purposes and only reviewed if an applicant advances to the final interview state. Reasonable accommodations are available upon request.

Technical Requirements:

We require that all employees have the following technical capability at their home: High speed internet over 10 Mbps and, specifically for all call center employees, the ability to plug in directly to the home internet router. These at-home technical requirements are subject to change with any scheduled re-opening of our office locations. 

Evolent is an equal opportunity employer and considers all qualified applicants equally without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, veteran status, or disability status. If you need reasonable accommodation to access the information provided on this website, please contact recruiting@evolent.com for further assistance.

The expected base salary/wage range for this position is $. This position is also eligible for a bonus component that would be dependent on pre-defined performance factors. As part of our total compensation package, Evolent is proud to offer comprehensive benefits (including health insurance benefits) to qualifying employees. All compensation determinations are based on the skills and experience required for the position and commensurate with experience of selected individuals, which may vary above and below the stated amounts.