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Evolent Health, Inc.
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ML Ops Engineer

ML Ops Engineer

Evolent Health, Inc.

2 - 5 years

4 - 8 Lacs

Pune

Posted:3 days ago| Platform:

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Skills Required

Health insurance Administration metadata HIPAA Agile Operations Monitoring Auditing

Work Mode

Work from Office

Job Type

Full Time

Job Description

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

bachelors 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

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ML Ops Engineer