Senior ML Engineer

Overview

This is a hands-on engineering role sitting inside a mature Data & ML Platform team. You'll take ML models from experimentation into reliable, observable production, owning deployment, serving, monitoring, and lifecycle management end to end. You'll work closely with Data Scientists, Data Engineers, and Platform Engineers, providing the engineering backbone that keeps production ML systems healthy and performing.

• Lead the deployment of ML models into production, ensuring high performance and clean integration with existing systems.
• Build and maintain scalable model serving solutions for both batch and real-time use cases.
• Design and own CI/CD pipelines covering automated build, test, validation, and deployment of ML artifacts.
• Support safe rollout and rollback strategies across environments.
• Translate feature definitions from data scientists into production-grade pipelines.
• Build and maintain feature serving infrastructure to keep training and inference environments consistent.
• Manage the full model lifecycle: registration, versioning, publishing, promotion, retraining, and retirement.
• Ensure production models meet latency, throughput, and reliability requirements.
• Implement logging, monitoring, and alerting across ML systems.
• Investigate and resolve issues related to model performance, deployments, and infrastructure.
• Contribute to operational documentation and runbooks.
• Drive adoption of engineering best practices across version control, data governance, and resource management.

• 7+ years in ML Engineering, MLOps, or Data Engineering.
• 4+ years hands-on with Databricks and MLflow.
• Strong Python skills;
  Scala or Java.
• Proven experience deploying ML models into production at scale.
• Solid understanding of ML system design, pipelines, and lifecycle management.
• Experience with AWS or Azure, Docker, and Kubernetes.
• Familiarity with Infrastructure as Code tooling Terraform or CDK.
• Degree in Computer Science, Engineering, or a related field.