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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.
Job Responsibility:
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
Requirements:
Bachelor’s or Master’s degree in Computer Science, Engineering, Mathematics, or related field (PhD is a plus)
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
Nice to have:
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