MLOps Engineer

Qualitest seeking a skilled MLOps Engineer. This is remote position offers an exciting opportunity to work on cutting-edge machine learning operations, model deployment, and cloud infrastructure management. You will play a key role in building, deploying, and maintaining scalable ML pipelines and production environments.

Key Responsibilities

• Design, develop, and maintain robust ML pipelines using tools such as Airflow, MLflow, and DVC.
• Manage containerized applications with Kubernetes and Docker to ensure scalable deployment.
• Automate infrastructure provisioning and configuration using Terraform.
• Implement continuous integration and continuous deployment (CI/CD) workflows with Git-based tools.
• Monitor and address data drift using tools like Evidently, Seldon Alibi, or equivalent.
• Package machine learning models leveraging ONNX, TorchServe, or TensorFlow Serving for production readiness.
• Collaborate closely with data scientists and engineers to optimize model lifecycle management.

Required Skills & Experience

• 5+ years in DevOps, MLOps, or Platform Engineering roles supporting ML workflows.
• Hands-on experience with Domino Data Lab for model management and deployment.
• Proficient in cloud infrastructure, preferably Oracle Cloud Infrastructure (OCI), including Compute, Object Storage, OCI Vault, VCN, and Oracle Kubernetes Engine (OKE).
• Strong scripting skills in Python, Bash, and shell scripting.
• Experience with Kubernetes, Docker, Terraform, Git, and CI/CD pipelines.
• Familiarity with ML pipeline orchestration and versioning tools like Airflow, MLflow, and DVC.
• Knowledge of model monitoring and drift detection tools such as Evidently or Seldon Alibi.
• Understanding model packaging technologies including ONNX, TorchServe, or TensorFlow Serving.

Education & Certifications

• Bachelor s or master s degree in computer science, Engineering, Data Science, or a related field.
• Certifications in OCI or Domino Data Lab are a plus but not mandatory.