Lead Machine Learning Engineer 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 a visionary Lead Machine Learning Engineer to architect, guide, and deliver enterprise-grade ML solutions that drive strategic business outcomes. You will lead cross-functional teams, define technical direction, and ensure the robustness, scalability, and reliability of ML systems across the full lifecycle.

As a Lead MLE, you will play a pivotal role in shaping our ML platform strategy, mentoring senior engineers, and driving adoption of best practices in MLOps, model governance, and responsible AI. You’ll collaborate with stakeholders across data science, engineering, and product to translate complex business challenges into intelligent systems.

Key Responsibilities

• Lead the design, development, and deployment of scalable ML models and pipelines for high-impact business applications.
• Architect ML systems using Vertex AI Pipelines, Kubeflow, Airflow, and manage infrastructure-as-code with Terraform/Helm.
• Define and implement strategies for automated retraining, drift detection, and model lifecycle management.
• Oversee CI/CD workflows for ML, ensuring reliability, reproducibility, and compliance.
• Establish standards for model monitoring, observability, and alerting across accuracy, latency, and cost.
• Drive integration of feature stores, vector databases, and knowledge graphs for advanced ML/RAG use cases.
• Ensure security, compliance, and cost-efficiency across ML pipelines and infrastructure.
• Champion MLOps best practices and lead initiatives for reproducibility, versioning, lineage tracking, and governance.
• Mentor and coach senior/junior engineers, fostering a culture of technical excellence and innovation.
• Stay ahead of emerging ML technologies and evaluate their applicability to UPS’s ecosystem.
• Collaborate with leadership, product managers, and domain experts to align ML initiatives with strategic goals.
• Contribute to long-term ML platform architecture and roadmap planning.
  
  

• 8+ years of experience in machine learning engineering, MLOps, or large-scale AI/DS systems.
• Proven track record of leading ML projects from conception to production.
• Deep expertise in Python (scikit-learn, PyTorch, TensorFlow, XGBoost) and SQL.
• Experience architecting ML systems in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML).
• Strong background in containerization (Docker, Kubernetes), orchestration (Airflow, TFX, Kubeflow), and infra-as-code (Terraform/Helm).
• Experience in big data and streaming technologies (Spark, Flink, Kafka, Hive, Hadoop).
• Hands-on experience with model observability tools (Prometheus, Grafana, EvidentlyAI) and Governance platforms (WatsonX).
• Strong understanding of ML algorithms, deep learning architectures, and statistical methods.
• Demonstrated leadership in mentoring teams and influencing technical direction.
  
  

• Experience with real-time inference systems or low-latency streaming platforms.
• Hands-on with enterprise ML platforms (IBM WatsonX, GCP Vertex AI) and feature stores.
• Knowledge of model interpretability and fairness frameworks (SHAP, LIME, Fairlearn).
• Expertise in data/model governance, lineage tracking, and compliance frameworks.
• Contributions to open-source ML/MLOps libraries or active participation in ML communities.
• Domain experience in logistics, supply chain, or large-scale consumer platforms.