About the Role
The role involves building and maintaining robust MLOps pipelines, supporting end-to-end machine learning workflows, and ensuring high availability and performance of deployed models.
Responsibilities
- Design and implement scalable machine learning infrastructure
- Develop automated pipelines for model training and deployment
- Monitor production models for performance and reliability
- Optimize CI/CD workflows for ML systems
- Ensure reproducibility and version control across ML experiments
- Collaborate with data scientists to transition models into production
- Maintain containerized environments for ML workloads
- Integrate monitoring and alerting systems for ML pipelines
- Support model validation and testing procedures
- Improve system reliability and reduce operational overhead
- Implement security and compliance standards for ML systems
- Troubleshoot issues across development and production environments
- Document architecture and operational procedures
- Evaluate and integrate new MLOps tools and frameworks
- Contribute to capacity planning for ML infrastructure
Nice to Have
- Experience in healthcare or regulated industries
- Background in data engineering or DevOps
- Knowledge of model explainability and fairness tools
- Experience with feature store systems
- Familiarity with model registry practices
- Exposure to real-time inference systems
- Understanding of data privacy principles
- Contributions to open-source MLOps projects
- Advanced degree in computer science or related field
Compensation
Competitive salary based on experience and qualifications
Work Arrangement
Hybrid work model with flexibility for remote and office-based work
Team
Collaborative team of data scientists, engineers, and DevOps specialists focused on AI-driven healthcare solutions
What We Offer
- Opportunities for professional development and training
- Flexible working hours and remote options
- Modern tech stack and development tools
- Supportive and inclusive work culture
- Health and wellness programs
Application Process
- Submit your resume and cover letter
- Initial screening by HR
- Technical assessment or coding challenge
- Interview with team members and technical lead
- Final interview with department leadership
Available for qualified international candidates
