Only experience in Dev ops profiles will not be considered
Job Title : MLOps Lead
Experience Required : 10-15 Years
Department : Machine Learning / AI Engineering
Reports To : Head of Data Science
Job Summary
We are seeking an experienced MLOps Lead to design, implement, and manage scalable machine learning infrastructure.
This role involves leading the deployment of ML models using Docker and Kubernetes, architecting end-to-end ML pipelines, and ensuring robust CI/CD practices for AI systems in production.
Key Responsibilities
- Design and implement scalable MLOps architecture for model training, validation, deployment, and monitoring
- Lead containerization of ML models using Docker and orchestrate deployments with Kubernetes
- Build and maintain CI/CD pipelines for ML workflows using tools like Jenkins, GitHub Actions, or GitLab CI
- Collaborate with data scientists and software engineers to streamline model integration into production
- Monitor model performance, automate retraining workflows, and manage model versioning
- Ensure infrastructure is secure, cost-efficient, and compliant with organizational standards
- Document architectural decisions and mentor junior MLOps engineers
- Evaluate and integrate tools for model governance, drift detection, and observability
Required Skills & Tools
- Strong experience with Docker and Kubernetes for container orchestration
- Proficiency in Python, Bash, and infrastructure-as-code tools like Terraform or CloudFormation
- Experience with MLflow, Kubeflow, or SageMaker for model lifecycle management
- Familiarity with cloud platforms (AWS, GCP, Azure) and their ML services
- Knowledge of CI/CD tools : Jenkins, GitHub Actions, GitLab CI
- Understanding of monitoring tools : Prometheus, Grafana, ELK stack
- Strong grasp of microservices architecture, API design, and networking fundamentals
Qualifications
- Bachelor's or Master's degree in Computer Science, Engineering, or related field
- 10-15 years of experience MLOps, or ML engineering roles
- Proven experience deploying ML models in production using Docker and Kubernetes
- Strong understanding of ML lifecycle and infrastructure design
Preferred Attributes
- Experience with model explainability, drift detection, and responsible AI practices
- Exposure to data versioning tools like DVC or Delta Lake
- Certification in cloud architecture or DevOps engineering
- Contributions to open-source MLOps tools or frameworks
(ref:hirist.tech)
