DevOps-MLOps Engineer
virtusa
Job Description
Required Qualifications
Bachelors or Master degree in Computer Science, Data Engineering, Machine Learning, or related field.
3 plus years of experience in DevOps, MLOps, or similar roles.
Proficiency with containerization (Docker), orchestration (Kubernetes), and Infrastructure as Code (Terraform, CloudFormation).
Experience with ML model deployment frameworks (TensorFlow Serving, TorchServe, FastAPI, BentoML, etc.).
Hands on experience with ML lifecycle tools (MLflow, Kubeflow, DVC, Airflow).
Strong scripting and programming skills (Python, Bash, etc.).
Experience with cloud platforms (AWS GCP Azure) and relevant ML services (SageMaker, Vertex AI, etc.).
Familiarity with data engineering workflows, streaming, and data pipelines (Kafka, Spark, etc.).
Strong understanding of CI CD concepts and tools (GitLab CI, Jenkins, ArgoCD).
Preferred Qualifications
Experience with monitoring and logging tools (Prometheus, Grafana, ELK, Datadog).
Knowledge of model governance, auditing, and regulatory requirements.
Exposure to A B testing, shadow deployments, and canary releases for ML models.
Certification in cloud platforms or DevOps practices is a plus.
Qualification
Required Qualifications
Bachelors or Master degree in Computer Science, Data Engineering, Machine Learning, or related field.
3 plus years of experience in DevOps, MLOps, or similar roles.
Proficiency with containerization (Docker), orchestration (Kubernetes), and Infrastructure as Code (Terraform, CloudFormation).
Experience with ML model deployment frameworks (TensorFlow Serving, TorchServe, FastAPI, BentoML, etc.).
Hands on experience with ML lifecycle tools (MLflow, Kubeflow, DVC, Airflow).
Strong scripting and programming skills (Python, Bash, etc.).
Experience with cloud platforms (AWS GCP Azure) and relevant ML services (SageMaker, Vertex AI, etc.).
Familiarity with data engineering workflows, streaming, and data pipelines (Kafka, Spark, etc.).
Strong understanding of CI CD concepts and tools (GitLab CI, Jenkins, ArgoCD).
Preferred Qualifications
Experience with monitoring and logging tools (Prometheus, Grafana, ELK, Datadog).
Knowledge of model governance, auditing, and regulatory requirements.
Exposure to A B testing, shadow deployments, and canary releases for ML models.
Certification in cloud platforms or DevOps practices is a plus.