MLOps Engineer

Educational Qualification:• B.Tech / M. Tech / M.S. in Computer Science, Data Engineering, AI or related discipline.• Certification in cloud DevOps or MLOps platforms (AWS DevOps Engineer, AzureDevOps Expert, GCP Professional ML Engineer) is highly desirable.• Contributions to MI-Ops or DevOps open source projects is preferred

Experience:• 7—10 years in machine learning operations or DevOps engineering.• Minimum 4 years building CVCD pipelines for AI/ML model deployment in enterprise orgovernment ecosystems.• Proven experience with containerized and microservice architectures.

Key Responsibilities:1.Design and manage continuous integration and delivery (CVCD) pipelines for AIIMLmodels across multiple environments.2. Establish model versioning, deployment, monitoring, and rollback mechanisms to ensurestability and traceability.3.Automate training, testing, and serving workflows using containerized solutions.4.Define infrastructure-as-code templates for scalable AI deployment on on-prem or cloud environments.5. Collaborate with Data Science and Engineering teams to standardize model input/outputformats and performance metrics.6.Implement logging, monitoring, and alerting for deployed models to ensure highavailability and accuracy over time.7.Ensure compliance with Responsible Al guidelines for deployment, including bias auditingand explainability tracking.

Technical Competencies:• MLOps Platforms: MLflow, Kubeflow, Azure ML, AWS SageMaker Pipelines, GCP VertexAl Pipelines for end-to—end ML workflow orchestration• Containerization: Docker, Kubernetes, Helm charts, container registries, and microservicesarchitecture for ML workloads• CI/CD: Jenkins, GitLab CI, GitHub Actions, Azure DevOps with specialized ML pipelineintegration and automated testing• Infrastructure-as-Code: Terraform, CloudFormation, Ansible for reproducible MLinfrastructure provisioning and management• Cloud Platforms: AWS (EKS, Lambda, ECR, S3), Azure (AKS, Container Registry, BlobStorage), GCP (GKE, Cloud Build, Cloud Storage)• Model Serving: TorchServe, TensorFlow Serving, Seldon, KServe, REST APIs, and real-time inference infrastructure.• Programming Languages: Python for automation, Bash scripting, YAML for configurationmanagement, basic understanding Of Go/Java• Database & Storage: Feature stores (Feast, Tecton), model registries, data versioning(DVC), and distributed storage systems• Workflow Orchestration: Apache Airflow, Prefect, Argo Workflows for complex MLpipeline scheduling and dependency management.