Key Responsibilities:DevOps Responsibilities
- Design, build, and maintain CI/CD pipelines for application and ML workloads
- Manage cloud infrastructure on GCP
- Configure and maintain containerized applications using Docker.
- Monitor system performance, availability, and security using monitoring tools
- Automate deployments, scaling, and system maintenance
- Collaborate with development and QA teams to streamline release cycles
MLOps Responsibilities
- Support end-to-end ML model lifecycle: training, versioning, deployment, and monitoring
- Deploy ML models using GCP services such as Vertex AI, AI Platform, or custom pipelines
- Implement model versioning, rollback, and performance monitoring
- Automate ML pipelines using Kubeflow, Airflow, or similar orchestration tools
- Work closely with Data Scientists to productionize ML models
Required Skills & Qualifications:Cloud & Infrastructure
- Strong experience with Google Cloud Platform (GCP)
- (GKE, Compute Engine, Cloud Storage, IAM, VPC, Cloud Build)
- Working knowledge of AWS (EC2, S3, IAM) is a plusDevOps Tools
- CI/CD tools: Jenkins, GitLab CI, GitHub Actions
- Containerization: Docker
- Monitoring & Logging: Prometheus, Grafana, GCP Monitoring
MLOps & Data
- Experience with ML model deployment and automation
- Tools: Vertex AI, MLflow, Airflow or similar Orchestration tools.
- Understanding of ML workflows, data pipelines, and model performance metrics
- Python scripting for automation and ML pipelines
Good to Have:
- Experience with Helm charts
- Knowledge of security best practices in cloud environments
- Exposure to microservices architecture
- Understanding of cost optimization on GCP
- Experience in production ML environments
Soft Skills:
- Strong problem-solving and troubleshooting skills
- Good communication and collaboration abilities
- Ability to work in fast-paced environments
- Ownership mindset and proactive approach
Job Types: Full-time, Permanent
Pay: ₹300,000.00 - ₹1,200,000.00 per year
Work Location: In person