Senior Machine Learning Engineer (Python, MLOps, GCP, Vertex AI)

About the role

UPS Technology's Machine Learning Engineering teams build intelligent systems that power its Smart Logistics Network, optimize UPS Airlines and support global transportation operations. The company is looking for a Senior Machine Learning Engineer to design, develop and deploy ML models and pipelines that drive business outcomes, working with data scientists, software engineers and product teams. The role covers the full ML lifecycle, from data exploration and feature engineering to training, evaluation, deployment and monitoring, and includes mentoring junior engineers.

Key responsibilities

• Design, deploy and maintain production-ready ML models and pipelines for real-world applications.
• Build and scale ML pipelines with Vertex AI Pipelines, Kubeflow and Airflow, and manage infrastructure as code with Terraform and Helm.
• Implement automated retraining, drift detection and redeployment of models.
• Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.
• Implement model monitoring, observability and alerting on accuracy, latency and cost.
• Integrate feature stores, knowledge graphs and vector databases for advanced ML and RAG use cases.
• Ensure pipelines are secure, compliant and cost-optimized, and adopt MLOps best practices including reproducibility, versioning and lineage tracking.
• Mentor junior engineers and contribute to the ML platform architecture and technical roadmap.
• Collaborate with product managers and data scientists to turn business problems into reliable ML systems.

Required profile

• Bachelor's or Master's degree in Computer Science, Engineering, Mathematics or a related field (PhD is a plus).
• 5+ years of experience in machine learning engineering, MLOps or large-scale AI/data science systems.
• Strong foundation in data structures, algorithms and distributed systems.
• Hands-on experience building and deploying ML models at scale in cloud environments.
• Experience with containerization and orchestration, CI/CD, infrastructure-as-code and big data/streaming technologies.
• Preferred: real-time inference systems, feature stores, model interpretability and fairness frameworks, open-source contributions or domain experience in logistics or supply chain.

Required skills

• Python (scikit-learn, PyTorch, TensorFlow, XGBoost) and SQL
• GCP Vertex AI, AWS SageMaker or Azure ML
• Docker, Kubernetes, Airflow, TFX, Kubeflow
• Terraform, Helm, GitHub Actions, GitLab CI, Jenkins
• Spark, Flink, Kafka, Hive, Hadoop
• Prometheus, Grafana, EvidentlyAI, WatsonX