Senior MLE Python ML Frameworks MLOps Containerization Terraform GCP Vertex AI IBM Watsonx

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Découvrez votre prochaine opportunité au sein dune organisation qui compte parmi les 500 plus importantes entreprises mondiales. Envisagez des opportunités innovantes, découvrez notre culture enrichissante et travaillez avec des équipes talentueuses qui vous poussent à vous développer chaque jour. Nous savons ce qu’il faut faire pour diriger UPS vers lavenir : des personnes passionnées dotées d’une combinaison unique de compétences. Si vous avez les qualités, de la motivation, de lautonomie ou le leadership pour diriger des équipes, il existe des postes adaptés à vos aspirations et à vos compétences daujourdhui et de demain.

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About Machine Learning Engineering at UPS Technology:

We’re the obstacle overcomers, the problem get-arounders. From figuring it out to getting it done… our innovative culture demands “yes and how!” We are UPS. We are the United Problem Solvers.

Our Machine Learning Engineering teams use their expertise in data science, software engineering, and AI to build next-generation intelligent systems. These systems power our Smart Logistics Network, optimize UPS Airlines, and enhance Global Transportation Operations. We build scalable, production-grade ML solutions that move up to 38 million packages a day (4.7 billion annually), delivering measurable impact across the enterprise.

About This Role

We are seeking passionate Senior Machine Learning Engineers to design, develop, and deploy ML models and pipelines that drive business outcomes. You’ll work closely with data scientists, software engineers, and product teams to build intelligent systems that are robust, scalable, and aligned with UPS’s strategic goals.

You will contribute across the full ML lifecycle—from data exploration and feature engineering to model training, evaluation, deployment, and monitoring. You’ll also help shape our MLOps practices and mentor junior engineers.

Key Responsibilities

• Design, deploy, and maintain production-ready ML models and pipelines for real-world applications.
• Build and scale ML pipelines using Vertex AI Pipelines, Kubeflow, Airflow, and manage infra-as-code with Terraform/Helm.
• Implement automated retraining, drift detection, and re-deployment of ML models.
• Develop CI/CD workflows (GitHub Actions, GitLab CI, Jenkins) tailored for ML.
• Implement model monitoring, observability, and alerting across accuracy, latency, and cost.
• Integrate and manage feature stores, knowledge graphs, and vector databases for advanced ML/RAG use cases.
• Ensure pipelines are secure, compliant, and cost-optimized.
• Drive adoption of MLOps best practices: develop and maintain workflows to ensure reproducibility, versioning, lineage tracking, governance.
• Mentor junior engineers and contribute to long-term ML platform architecture design and technical roadmap.
• Stay current with the latest ML research and apply new tools pragmatically to production systems.
• Collaborate with product managers, DS, and engineers to translate business problems into reliable ML systems.
  
  

• 5+ years of experience in machine learning engineering, MLOps, or large-scale AI/DS systems.
• Strong foundations in data structures, algorithms, and distributed systems.
• Proficient in Python (scikit-learn, PyTorch, TensorFlow, XGBoost, etc.) and SQL.
• Hands-on experience building and deploying ML models at scale in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).
• Experience with containerization (Docker, Kubernetes) and orchestration (Airflow, TFX, Kubeflow).
• Familiarity with CI/CD pipelines, infrastructure-as-code (Terraform/Helm), and configuration management.
• Experience with big data and streaming technologies (Spark, Flink, Kafka, Hive, Hadoop).
• Practical exposure to model observability tools (Prometheus, Grafana, EvidentlyAI) and governance (WatsonX)
• Strong understanding of statistical methods, ML algorithms, and deep learning architectures.
  
  

• Experience with real-time inference systems or low-latency streaming platforms (e.g. Kafka Streams).
• Hands-on with feature stores and enterprise ML platforms (IBM WatsonX, Vertex AI).
• Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn) and responsible AI principles.
• Strong understanding of data/model governance, lineage tracking, and compliance frameworks.
• Contributions to open-source ML/MLOps libraries or strong participation in ML competitions (e.g., Kaggle, NeurIPS).
• Domain experience in Logistics, supply chain, or large-scale consumer platforms.