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