MLOps Engineer
Nimblemind.ai
United States
Posted on Jun 11, 2025
About Us
Nimblemind.ai is revolutionizing healthcare by empowering providers to harness the full potential of clinical data. Our cloud-based SaaS platform simplifies data integration, structuring, and labeling, allowing healthcare providers to maintain security and privacy while unlocking AI-driven insights. Our mission is to enable smarter and more personalized patient care through robust Healthcare AI.
Role Overview
We’re seeking a MLOps Engineer to help us operationalize machine learning across our end-to-end AI pipeline. You’ll work side-by-side with AI/ML engineers, clinical specialists, and platform teams to support training, packaging, deployment, and monitoring of our clinical models. You’ll get direct exposure to production-grade AI/ML systems, and play a critical role in delivering real-world impact in healthcare.
Key Responsibilities
Model Lifecycle Support
Nimblemind.ai is revolutionizing healthcare by empowering providers to harness the full potential of clinical data. Our cloud-based SaaS platform simplifies data integration, structuring, and labeling, allowing healthcare providers to maintain security and privacy while unlocking AI-driven insights. Our mission is to enable smarter and more personalized patient care through robust Healthcare AI.
Role Overview
We’re seeking a MLOps Engineer to help us operationalize machine learning across our end-to-end AI pipeline. You’ll work side-by-side with AI/ML engineers, clinical specialists, and platform teams to support training, packaging, deployment, and monitoring of our clinical models. You’ll get direct exposure to production-grade AI/ML systems, and play a critical role in delivering real-world impact in healthcare.
Key Responsibilities
Model Lifecycle Support
- Assist in automating ML workflows via Vertex AI Pipelines
- Help log and monitor training runs using Vertex AI Experiments & TensorBoard
- Support model packaging and deployment to Vertex AI Endpoints
- Contribute to ETL workflows that ingest and preprocess EMR, wearable, and imaging data
- Organize datasets in GCS using our standardized folder and bucket conventions
- Learn to manage CI/CD workflows with Cloud Build and GitHub
- Monitor model health using Vertex AI Model Monitoring and Model Registry
- Help detect and document data drift and performance degradation
- Collaborate on retraining pipelines to ensure production model quality
- Participate in debugging sessions, peer reviews, and code walkthroughs
- Support senior MLOps and ML engineers with experiment tracking, logging, and deployment readiness
- Learn cloud-native best practices for AI infrastructure and model scalability
- B.S. or M.S. in Computer Science, Data Engineering, Biomedical Informatics, or related field
- Solid Python programming skills and basic experience with Git
- Exposure to cloud environments (preferably GCP) and ML libraries (scikit-learn, TensorFlow, PyTorch)
- Strong interest in MLOps, healthcare AI, and model deployment workflows
- A growth mindset and ability to work independently on well-scoped tasks
- Hands-on experience with Vertex AI tools or similar MLOps platforms
- Exposure to healthcare data (EMR, imaging, wearables) through coursework or projects
- Familiarity with explainability tools (SHAP, LIME) and ML observability concepts
- Experience with Docker, cloud storage buckets, or orchestration tools like Airflow/Kubeflow
- Be part of a mission-driven company shaping the future of healthcare.
- Lead the development of groundbreaking products while contributing directly to their design & creation.
- Collaborate with a world-class team of healthcare and technology innovators.
- Competitive compensation package, including equity in a fast-growing startup.
