As an MLOps Engineer, you will design, build, and maintain the infrastructure, pipelines, and tools that enable seamless machine learning deployment and operation across enterprise environments. You’ll bridge the gap between Machine Learning and Software Engineering, owning ML workflows end-to-end from automated training and versioning to production monitoring and continuous integration.

Functional Responsibilities:

• Build and automate continuous integration and deployment (CI/CD) pipelines tailored for machine learning models and data workflows using tools like GitHub Actions, GitLab CI, or Jenkins.
• Architect and manage containerized microservices and orchestration platforms for ML workloads using Docker and Kubernetes (EKS, GKE, AKS).
• Implement and manage ML lifecycle, tracking, and versioning tools such as MLflow, DVC, or Kubeflow.
• Provision, configure, and maintain ML cloud infrastructure using Infrastructure as Code (IaC) tools like Terraform.
• Establish comprehensive monitoring, alerting, and logging systems for deployed models using tools like Prometheus, Grafana, or Arize to track data drift, model performance, and operational health.
• Collaborate with data scientists and software engineers to transition ML models from experimental scripts into secure, low-latency, and scalable production APIs.

Qualifications:

• 5+ years of hands-on experience in MLOps, with a proven track record of deploying and scaling ML pipelines in enterprise environments.
• Advanced English proficiency (written and spoken) with strong communication skills to collaborate effectively across multidisciplinary teams.
• Strong Python and Bash scripting skills, with expertise in containerization (Docker) and orchestration frameworks (Kubernetes).
• Deep hands-on experience with cloud platforms (AWS, GCP, Azure), Infrastructure as Code (Terraform), and CI/CD pipelines.
• Demonstrated experience with dedicated MLOps platforms (MLflow, DVC, Kubeflow) and model observability/monitoring solutions (Prometheus, Grafana, Arize).