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
MLOps Lead - Docker/Kubernetes • Delhi, IN
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