Machine Learning Operations Engineer

Machine Learning Operations (MLOps) Engineer
The MLOps Engineer will be responsible for designing, implementing, and supporting the infrastructure and operational processes required to deploy, monitor, and maintain machine learning models in production. Working closely with data scientists, data engineers, and business stakeholders, this role ensures that predictive models—such as buy quantity optimization, demand forecasting, assortment planning, pricing, and inventory optimization—are deployed efficiently, monitored continuously, and retrained as business conditions evolve.
The ideal candidate will have experience building scalable MLOps platforms, automating machine learning pipelines, and establishing best practices for model governance, reproducibility, and operational excellence in a cloud-based environment.
Key Responsibilities:
Design, develop, and maintain end-to-end MLOps pipelines for machine learning model training, validation, deployment, and monitoring.
Build and manage CI/CD pipelines for machine learning applications to enable reliable and automated model releases.
Implement model versioning, experiment tracking, artifact management, and reproducible deployment processes.
Monitor production models for performance, latency, data quality, concept drift, and model drift, and initiate retraining workflows when required.
Develop automated retraining and model promotion pipelines based on business rules and monitoring thresholds.
Collaborate with Data Scientists to operationalize predictive models for demand forecasting, buy quantity optimization, inventory planning, allocation, and other retail analytics use cases.
Integrate machine learning solutions with enterprise data platforms, APIs, and downstream business applications.
Ensure production ML environments meet security, governance, compliance, and scalability requirements.
Troubleshoot production issues, optimize inference performance, and improve deployment reliability.
Document deployment processes, operational runbooks, monitoring dashboards, and platform standards.
Partner with DevOps, Cloud Engineering, and Data Engineering teams to continuously improve ML platform capabilities.
Required Skills & Experience:
Strong experience implementing MLOps frameworks on cloud platforms such as AWS, Azure, or Google Cloud.
Experience with containerization and orchestration technologies including Docker and Kubernetes.
Hands-on experience with ML lifecycle tools such as MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, or similar platforms.
Experience building CI/CD pipelines using GitHub Actions, Azure DevOps, Jenkins, GitLab CI, or equivalent tools.
Proficiency in Python and experience developing automation scripts for machine learning workflows.
Experience with workflow orchestration tools such as Airflow, Prefect, Dagster, or similar.
Strong understanding of model monitoring, feature engineering pipelines, model versioning, and data validation.
Experience integrating machine learning solutions with enterprise data warehouses, data lakes, and streaming platforms.
Familiarity with Infrastructure as Code (Terraform, CloudFormation, or similar) is preferred.
Strong problem-solving skills and the ability to support production AI systems in a fast-paced retail environment.
Preferred Retail Experience:
Experience supporting production machine learning models for:
Buy Quantity Optimization
Demand Forecasting
Inventory Optimization
Allocation & Replenishment
Pricing & Markdown Optimization
Customer Segmentation and Personalization
Understanding of retail planning systems, merchandising processes, and supply chain analytics is highly desirable.

Work arrangement
Yes