We are seeking an experienced and driven Machine Learning / MLOps Engineer to join our engineering team. In this role, you will bridge the gap between machine learning research and production systems. You will be responsible for developing, fine-tuning, deploying, and scaling state-of-the-art machine learning and foundation models in production. If you are passionate about building robust MLOps infrastructure, optimizing multi-GPU workloads, and collaborating with cross-functional teams to deliver high-impact AI solutions, we want to hear from you. Req# Responsibilities Model Development & Production: Develop, fine-tune, and deploy machine learning and foundation models into robust production environments MLOps & Pipeline Engineering: Build, scale, and operate end-to-end MLOps pipelines, including CI/CD for ML, model lifecycle management, monitoring, and observability Cloud & Infrastructure Management: Deploy and manage ML workloads on Azure and Kubernetes (AKS), ensuring high availability, scalability, and security Performance Optimization: Manage multi-GPU-based workloads and optimize model performance, latency, and resource utilization Software Engineering Excellence: Uphold rigorous software engineering standards, including automated testing, high code quality, CI/CD pipelines, and infrastructure as code Cross-Functional Collaboration: Partner closely with ML researchers, data scientists, and engineering teams to translate research breakthroughs into production-ready systems Requirements Production ML Experience: Hands-on experience developing, fine-tuning, and deploying ML and foundation models in production environments Programming & Frameworks: Strong Python programming skills with deep expertise in PyTorch and/or TensorFlow MLOps Expertise: Proven experience building and operating MLOps pipelines (CI/CD, model tracking, registry, monitoring) Cloud & Containerization: Experience deploying ML workloads on Azure and Kubernetes (AKS) Software Engineering: Strong background in software engineering best practices (unit/integration testing, version control, code quality, automation) Hardware & Performance: Experience managing multi-GPU-based workloads and optimizing inference/training performance Communication & Teamwork: Ability to work effectively and collaboratively with ML researchers, data scientists, and cross-functional engineering teams
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.
- Dice Id: 10330481
- Position Id: 13438e6c0b737be337adc35dc2df19d2
- Posted 12 hours ago
