AI/ML Platform Engineer

Responsibilities

• Design and maintain scalable platforms supporting machine learning model development, testing, deployment, and monitoring.
• Build automation frameworks that improve reliability, reproducibility, and operational efficiency.
• Develop CI/CD processes for machine learning and production software environments.
• Manage containerized workloads and distributed compute resources across cloud and hybrid infrastructure.
• Create and optimize data ingestion, transformation, and processing pipelines for large-scale datasets.
• Collaborate with engineers, data scientists, and research stakeholders to enable efficient experimentation and model development.
• Implement observability, monitoring, and performance management solutions.
• Automate infrastructure provisioning and configuration management using modern DevOps practices.
• Support secure, scalable, and cost-effective AI/ML operations.
• Contribute to platform architecture decisions and technical best practices.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related field, or equivalent experience.
• 5+ years of experience in software engineering, platform engineering, DevOps, cloud engineering, or related disciplines.
• Experience supporting machine learning, analytics, or data-driven technology environments.
• Strong knowledge of containerization and orchestration technologies.
• Experience developing CI/CD pipelines and infrastructure automation solutions.
• Proficiency in Python and modern software development practices.
• Experience building scalable data processing pipelines.
• Familiarity with one or more major cloud platforms.
• Knowledge of distributed computing concepts and large-scale processing environments.
• Strong troubleshooting, performance optimization, and operational support skills.

Preferred Qualifications

• Experience supporting research-oriented, scientific, healthcare, or advanced analytics environments.
• Exposure to modern AI/ML frameworks and model lifecycle management.
• Experience working with structured and unstructured data at scale.
• Familiarity with workflow orchestration and infrastructure-as-code approaches.
• Understanding of GPU-accelerated computing environments.
• Experience supporting large-scale model training or advanced data processing workloads.