Senior Machine Learning Engineer (Production & MLOps Focus)

Our client is an American multinational shipping & receiving and supply chain management company founded in 1907. Operating in more than 220 countries and territories, they are committed to moving the world forward by delivering what matters. Beginning as a small messenger service and a $100 loan, they now have more than 500,000 users around the globe. They are a transportation and logistics leader, offering innovative solutions to customers, big and small. They understand and appreciate their responsibility to help build safer, stronger and more resilient communities founded on justice and economic opportunity for all, supported by a healthy, sustainable global environment.

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. 

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.