Team Lead MLops
Required work experience: 3–6 years
Full-time, full day
Full-time, full day
Main Responsibilities:
• Develop and implement MLOps strategies to support the full lifecycle of ML models
• Design and optimize automated processes for training, testing, and deploying models
• Integrate machine learning solutions with existing systems and services
• Create metrics and monitoring systems to evaluate ML model performance in production
• Collaborate closely with Data Scientists, DevOps, and engineering teams to ensure the uninterrupted operation of ML systems.
• Implement the best practices and standards to ensure the reproducibility of experiments and models.
• Design and optimize automated processes for training, testing, and deploying models
• Integrate machine learning solutions with existing systems and services
• Create metrics and monitoring systems to evaluate ML model performance in production
• Collaborate closely with Data Scientists, DevOps, and engineering teams to ensure the uninterrupted operation of ML systems.
• Implement the best practices and standards to ensure the reproducibility of experiments and models.
Required Skills and Qualifications:
• 4+ years of experience in MLOps/DevOps
• 2+ years of team leadership experience (developers or engineers)
• Strong knowledge of CI/CD principles for ML systems and hands-on implementation
• Proficient with containerization (Docker) and orchestration (Kubernetes)
• Skilled in ML lifecycle tools: MLflow, Kubeflow, Airflow
• Understanding of monitoring and debugging ML models in production
• Experience automating model testing and validation workflows
• Familiarity with version control for code and models (Git, DVC)
• Solid Python skills; experience with ML frameworks like TensorFlow and PyTorch
• Knowledge of data/model security best practices
• 2+ years of team leadership experience (developers or engineers)
• Strong knowledge of CI/CD principles for ML systems and hands-on implementation
• Proficient with containerization (Docker) and orchestration (Kubernetes)
• Skilled in ML lifecycle tools: MLflow, Kubeflow, Airflow
• Understanding of monitoring and debugging ML models in production
• Experience automating model testing and validation workflows
• Familiarity with version control for code and models (Git, DVC)
• Solid Python skills; experience with ML frameworks like TensorFlow and PyTorch
• Knowledge of data/model security best practices
Perks and Benefits:
• Hybrid work model and flexible hours
• Professional growth: trainings, conferences, demo days, hackathons
• 28 calendar days of annual leave (including 4 extra days)
• Annual KPI-based performance bonus
• Corporate mobile plan
• Voluntary health insurance with dental and medication coverage, including free coverage for two family members
• Discounted and installment-based gym memberships
• Compensation for using a personal laptop
• Free access to an online mental health support platform
• Access to MyBook corporate library
• Professional growth: trainings, conferences, demo days, hackathons
• 28 calendar days of annual leave (including 4 extra days)
• Annual KPI-based performance bonus
• Corporate mobile plan
• Voluntary health insurance with dental and medication coverage, including free coverage for two family members
• Discounted and installment-based gym memberships
• Compensation for using a personal laptop
• Free access to an online mental health support platform
• Access to MyBook corporate library
Join us!
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