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