Machine Learning Operations Engineer

At OPSWAT’s Technology Development Centre in Veszprém, we are looking for talented Machine Learning Operations Engineers to join our teams developing and deploying advanced AI-driven cybersecurity solutions that protect critical infrastructure across the globe. If you are knowledgeable, creative, and passionate about building reliable, scalable, and efficient ML pipelines and infrastructure - enabling data scientists to deliver innovative models seamlessly into production - then you could be exactly who we’re looking for! 

In Veszprém, in the heart of a peaceful, friendly, historic city, we have created a modern, inspiring office environment that encourages collaboration, where friendly, direct relationships between colleagues and high-quality professional work are equally important. Using modern, innovative technologies, we work on products that truly contribute to making our world safer at one of the world's leading cybersecurity companies.  We support professional development through training, mentoring, and real opportunities for advancement. The Veszprém office offers the perfect balance of calm and professionalism – a modern environment, friendly people, and a strong team spirit.
If you want to work in a place where your knowledge is valued and you are treated as a person, then OPSWAT Veszprém is the place for you.

At OPSWAT, we believe that great talent is always worth getting to know, even when there are no current vacancies. Our Evergreen Machine Learning Operations Engineer position offers continuously open opportunities for enthusiastic and motivated professionals who may join us in the future. It is not tied to a specific vacancy, but it enables us to get to know you and stay in touch, so that when a new opportunity arises, you could be among the first people we reach out to.  

If you’re interested in working as a Machine Learning Operations Engineer at OPSWAT, please apply, and we will get in touch!

• Build and maintain robust CI/CD pipelines for ML models and data workflows
• Automate and manage the end-to-end lifecycle of machine learning models, from training to deployment and monitoring
• Develop infrastructure to support scalable, reproducible model training and serving in Cloud or on-premise environments
• Monitor model performance in production, detect model/data drift, and implement alerting systems
• Collaborate with Data Scientists and DevOps Engineers to operationalize ML models and integrate them into products
• Drive Infrastructure-as-Code (IaC) and DevOps best practices in ML environments
• Identify and troubleshoot production issues with ML models
• Collaborate with cross-functional and product teams to identify, develop, and test out new ideas
• Build and nourish a culture of data-driven decision making, influencing the mindset of colleagues and management as well

• 3+ years of experience in MLOps, DevOps, or ML Engineering
• Strong programming skills in Python and Bash
• Experience with ML frameworks as training/serving (e.g., Ray, Sagemaker jobs)
• Knowledge of monitoring tools and best practices for production ML systems(eg: Tensorboard, Datadog,Streamlit)
• Hands-on experience with CI/CD tools (e.g., GitHub Actions, Bitbucket pipelines, TeamCity)
• Proficiency with containerization and orchestration (Docker, Kubernetes)
• Experience with Cloud platforms (AWS, GCP, or Azure) and ML services (e.g., SageMaker, Vertex AI, MLflow, Kubeflow) and automation solutions (terraform / ansible / others)
• Knowledge of monitoring tools and best practices for production ML systems
• Solid understanding of the ML model lifecycle, versioning, and reproducibility
• Strong communication skills and the ability to work cross-functionally
• Experience with Linux systems
• Excellent troubleshooting skills and a commitment to document findings

• Stable, growing international company background with an exceptional customer group 
• Opportunity to improve your professional skills 
• The newest technology environment 
• Language course and opportunity for active recreation – kettlebell, football and office massage 
• Attractive working environment – nice office full of accessories (fruits every day, coffee, breakfast, tea etc.)
• Regular team events and Happy Hour activities