We are seeking a highly skilled MLOps Engineer to design, implement, and manage the infrastructure and deployment pipelines for Machine Learning (ML) systems. The ideal candidate will have deep expertise in CI/CD, container orchestration, cloud platforms, and observability tools to ensure performance, scalability, and reliability of AI-driven workflows.

Key Responsibilities
ML Infrastructure & Deployment: Implement best practices in MLOps, including automated model deployment, versioning, and monitoring using tools such as Kubernetes, Docker, MLflow, Kubeflow, and Azure ML.
CI/CD Pipeline Development: Design, implement, and maintain CI/CD pipelines for ML systems using tools such as Jenkins, GitHub Actions, or GitLab CI.
Performance & Reliability Monitoring: Continuously monitor and optimize the performance, scalability, and reliability of ML systems.
Infrastructure Scaling: Build and scale infrastructure to support AI workflows across development, staging, and production settings.
Observability & Monitoring: Deploy and manage observability tools (Prometheus, Grafana,



ELK Stack, Azure Monitor) for real-time system health tracking and alerting.
Vector Database Management: Set up and maintain infrastructure for vector databases (e.g., Pinecone, Weaviate, Milvus) to support AI-driven applications.

Requirements
Required Skills & Qualifications
Experience: Minimum 3+ years in DevOps/Infrastructure and 2+ years in MLOps.
ML Tools & Frameworks: Experience with MLflow, Kubeflow, and model lifecycle management.
CI/CD Expertise: Hands-on experience with Jenkins, GitHub Actions, GitLab CI, Azure DevOps.
Programming & Scripting: Proficiency in Python, Bash/Shell scripting, and YAML for automation and configuration.
Containerization & Orchestration: Solid experience with Docker, Kubernetes, Helm.
Infrastructure as Code (IaC): Proficiency in Terraform, Ansible, or CloudFormation.
Cloud Platforms: Expertise in AWS, GCP, or Azure (preferred: Azure M

📌 Mlops Engineer Pune (India)
🏢 Agivant Technologies India
📍 India