Software Engineer (ML Ops, AI Model Operationalization)

We are looking for a skilled ML Ops Engineer to help bring AI models into real-world production environments at scale. In this role, you will not focus on training models or fine tuning LLMs; your primary responsibility will be to operationalize, deploy, monitor, and maintain AI/ML solutions, ensuring they are robust, reliable, and integrated into business processes. In daily work you’ll collaborate closely with team of data scientists, data engineers and software engineers to ensure that models are securely deployed, continuously integrated, and ready for scalable production use.

This role is about application development, engineering- and infrastructure-focused, with a strong emphasis on production-readiness, automation, and operational excellence.

How you will get the job done

• packaging, deploying, and monitoring machine learning models in production environments using Azure ML, MLflow, and containerization technologies
• designing and maintaining scalable infrastructure using Azure Kubernetes Service (AKS), Docker, and CI/CD pipelines
• building APIs, endpoints, and inference pipelines to serve models efficiently and reliably
• implementing monitoring, logging, and alerting for ML services to ensure uptime, performance, and model accuracy
• managing versioning of models, artifacts, and configurations, ensuring traceability and compliance with governance standards
• collaborating closely with Data Scientists to understand model requirements and with DevOps teams to integrate ML workflows into broader CI/CD frameworks
• driving automation across the ML lifecycle, including testing, deployment, retraining triggers, and rollback mechanisms
• ensuring that AI solutions meet enterprise security, compliance, and data privacy requirements
• writing clean, efficient, and maintainable Python code, following modern software engineering practices including testing, version control, and documentation

Let's be healthy – medical package, sports card, and numerous sports sections – these are some of the benefits that help our employees stay in good shape.

Let's be balanced – work-life balance is a key aspect of a healthy workplace. We offer our employees flexible working hours, a confidential employee assistant program, as well as the possibility of remote working. However, staying at home with our in-office gaming room and dog-friendly office in Warsaw won’t be easy.

Let's be smart – we organize numerous workshops and training courses. Thanks to hackathons and meetups, our specialists share their expertise with others. Additionally, we have a wide range of digital learning platforms and language courses.

Let's be responsible – each year, we participate in several CSR activities, during which, together with our colleagues, we do our best to create a better future.

Let's be fun – company-wide bike races and soccer matches, film marathons in our cinema room or other engaging team-building activities – we got it covered!

Let's be diverse – every team member is valued, regardless of gender, nationality, religious beliefs, disability, age, and sexual orientation or identity. Your qualifications, experience, and mindset are our greatest benefit!

• fluency in English
• 3+ years of experience as a software developer or backend engineer, ideally with exposure to cloud-native and distributed systems
• expert-level knowledge of Python, particularly in building production-grade software and ML/AI pipelines
• hands-on experience with Azure ML, Azure DevOps, Azure Container Registry, and Azure Key Vault
• proficiency in Docker and Kubernetes (AKS) for deploying scalable ML solutions
• experience in building and maintaining CI/CD pipelines for ML applications, including automated testing and deployment
• familiarity with tools such as Prometheus, Grafana, Azure Monitor, or similar for monitoring and logging
• experience with MLflow or similar tools for experiment tracking, model registry, and reproducibility
• proven ability to work effectively within cross-functional teams in an Agile/Scrum setup

Nice to have

• experience with Databricks or other managed ML platforms
• familiarity with model drift detection and automated retraining pipelines
• knowledge of vector databases and inference optimization
• understanding of AI-specific security and compliance practices