Senior MLOps Engineer (Ray.io)

N-iX is a global software development service company that helps businesses across the world develop successful software products. Founded in 2002, N-iX has come a long way, expanding its presence across Europe, the US, and Latin America. Today, we are a strong community of 2,000+ professionals and a reliable partner for global industry leaders and Fortune 500 companies.

Our client is a global commerce leader where you can influence how the world buys, sells, and gives. You'll be part of a work culture that's been genuinely committed to diversity and inclusion since its founding over twenty five years ago. Here, you can be yourself, do your best work along with a team of professionals, and have a meaningful impact on people across the globe. We seek people with drive, ideas, and a passion for helping small businesses succeed to help.

N-iX is looking for a Senior AI/ML Platform Engineer to join our AI Platform Team. You will work on building and scaling the next-generation AI infrastructure, helping researchers and data scientists run training and inference efficiently. A key focus is Ray.io and distributed ML workloads.

Responsibilities:

• Build and support ML infrastructure for training and inference at scale (Ray.io, PyTorch, TensorFlow).
• Partner with infrastructure and security teams to ensure high availability (99.999%) and reliability.
• Troubleshoot and resolve production issues (performance, compatibility, framework upgrades).
• Automate deployment, monitoring, and CI/CD pipelines (Kubernetes, Docker, Jenkins).
• Deliver solutions that accelerate engineers' work through automation.
• Contribute to documentation and best practices for MLOps.
• Collaborate with internal teams and researchers to diagnose and resolve technical issues.
• Provide guidance and knowledge-sharing sessions to improve operational excellence.

Requirements:

• 5+ years of experience in Python software engineering.
• Hands-on experience with Ray.io for distributed training/inference.
• Strong background with ML frameworks: PyTorch, TensorFlow, Triton.
• Solid knowledge of Kubernetes, Docker, Linux fundamentals.
• Experience with DevOps practices (CI/CD, test automation, monitoring).
• Good debugging and triaging skills.
• Strong communication and collaboration abilities in English.

Would be a plus:

• Experience with LLM fine-tuning and inference optimization.
• Previous work with AI/ML infrastructure at scale.