MLOps Engineer (AI Infrastructure)

Description

We are monday.com, a global software company transforming how businesses run. Our product suite can adapt to the needs of diverse industries and use cases within one powerful platform, empowering ~250,000 customers worldwide to reimagine how work gets done, drive greater efficiency, and scale like never before.

With over 2,900 employees across the globe, we grow by prioritizing transparency and knowledge sharing. We care about the impact you make, not the hours you clock, so we encourage initiative, ownership, and fresh thinking. We back our people with flexible work, wellness and mental health support, and a work environment built on collaboration.

We’re looking for an MLOps Engineer to build the infrastructure that keeps our ML models production-grade, cost-efficient, and reliable at scale. You’ll work on everything from observability and pipelines to disaster recovery, ensuring that our AI investments are always serving customers in real time.

We fully embrace the AI revolution and equip you with AI-powered IDEs, customizable agent rules, prompt engineering tools, and AI-infused CI/CD pipelines designed to boost speed and reliability. You'll also tap into AI-driven insights, helping you make smarter decisions, faster.

The role is based in our Warsaw office - established in 2022, it is a growing hub for engineers who love solving impactful problems. Teams here work on a broad range of challenges that push the boundaries of our products and infrastructure.

About The Role

• Build and maintain a platform for model deployment integrated with monday.com standards (authentication and authorization, observability, disaster recovery).
• Design and implement observability, cost projections, and release pipelines with automated testing, canary deployments, and rollback strategies.
• Ensure ML models are reliable, scalable, and always production-ready.
• Serve as on-call support for ML models, fixing issues and handling alerts.
• Collaborate with AI engineers and data scientists to move research into production-grade services.
• Lead future-facing efforts: fine-tuning models on monday.com data, balancing cost vs. performance, and optimizing GPU/CPU usage.
• Drive continuous performance optimization - from tokenization pipelines to GPU parallelization strategies.

Requirements

• 5+ years of experience as an MLOps, Infrastructure Engineer, or DevOps with ML focus.
• Proven experience deploying and maintaining machine learning models in production at scale.
• Strong knowledge of CI/CD pipelines, observability, testing, and rollback strategies.
• Hands-on expertise with cloud infrastructure (AWS preferred), GPUs, and containerized services.
• Experience with cost monitoring and optimization for large-scale ML systems.
• Comfort working with data scientists and ML engineers, translating research into robust services.
• Bonus: Experience with fine-tuning models, managing training data, and balancing performance vs. cost trade-offs.

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