AWS DevOps Engineer Dataviv Media Network
Dataviv Media Network
Office Location
Full Time
Experience: 5 - 5 years required
Pay:
Salary Information not included
Type: Full Time
Location: Haryana
Skills: Docker, Kubernetes, Helm, Python, Jenkins, aws, GCP, Azure, FastAPI, cicd, Terraform, ML ops, Github Actions, GitLab CI, MLflow, DVC, Kubeflow, Seldon
About Dataviv Media Network
Job Description
You are an experienced Senior DevOps/MLOps Engineer who will be responsible for leading and managing a high-performing engineering team. Your main focus will be on overseeing the deployment and scaling of machine learning models and backend services using modern DevOps and MLOps practices. It is essential that you have proficiency in FastAPI, Docker, Kubernetes, and CI/CD. Your key responsibilities will include guiding and managing a team of DevOps/MLOps engineers, optimizing, containerizing, and deploying FastAPI applications at scale, managing infrastructure using tools like Terraform or Helm, handling multi-environment Kubernetes clusters (GKE, EKS, AKS, or on-prem), managing ML model lifecycle including versioning, deployment, monitoring, and rollback, designing and maintaining robust CI/CD pipelines for model and application deployment, setting up observability tools such as Prometheus, Grafana, ELK, ensuring secure infrastructure and data pipelines. Your required skills should include a deep understanding of building, scaling, and securing APIs with FastAPI, expert-level experience in Docker and Kubernetes for containerization and orchestration, familiarity with CI/CD tools like GitHub Actions, GitLab CI, Jenkins, ArgoCD, or similar, experience with cloud platforms such as AWS/GCP/Azure, strong scripting and automation skills with Python, and knowledge of ML Workflow Tools like MLflow, DVC, Kubeflow, or Seldon. Preferred qualifications for this role include experience in managing hybrid cloud/on-premise deployments, strong communication and mentoring skills, and an understanding of data pipelines, feature stores, and model drift monitoring. This is a full-time, permanent position that requires in-person work location.,