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# 1122672674 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