MLOps Engineer

EyeSpy Recruitment; Polska; On-site; PLN 150,000 - 230,000; Full time

Job summary

EyeSpy Recruitment is seeking an experienced MLOps Engineer to design, build and operate scalable ML platforms, pipelines and serving systems for a production-grade environment used by millions of players.

You will own the release flow, automate data and model pipelines, monitor APIs, and develop internal tooling and observability dashboards to keep systems healthy and secure in production.

Qualifications

• Strong Python skills and solid software development experience.
• Experience deploying ML models in production.
• Cloud platforms experience, ideally Microsoft Azure.
• Docker and containerisation for scalable deployments.
• Monitoring, logging and alerting for ML systems.
• Git, package management and dependency tracking.
• Data modelling, ETL and SQL/NoSQL databases.
• Understanding of core ML concepts and algorithms.
• Degree in Computer Science, Engineering or related field.

Responsibilities

• Serve models at scale.
• Automate data, training and inference pipelines.
• Own the release flow with CI/CD for testing, validation and deployment.
• Keep APIs healthy with monitoring and security.
• Build internal tools to configure and manage ML systems in production.
• Develop observability dashboards and alerts for model and infra health.
• Write unit and integration tests for ML code, pipelines and deployment workflows.
• Ship securely with containerised deployments and data handling best practices.

Skills

Python

Deploying ML models in production

Monitoring/logging/alerting

Git

Data modelling / ETL

SQL / NoSQL databases

Machine learning concepts

Education

Bachelor's or Master's in Computer Science / Engineering or related field

Tools

Docker

Microsoft Azure

Job description

Anyone can train a model. Keeping it fast, reliable and live for millions of players is the hard part, and that's your job.

We're hiring an MLOps Engineer for a global gaming technology company. You'll build the platforms, pipelines and serving systems that take machine learning from experiment to production, and keep it running.

What you'll actually do

• Serve models at scale. Design and run scalable inference and serving systems for ML workloads.
• Automate everything. Build and maintain automated data, training and inference pipelines.
• Own the release flow. Build and manage CI/CD pipelines for testing, validation and deployment.
• Keep APIs healthy. Monitor deployed APIs for performance, reliability and security.
• Build the platform. Create internal tools that teams use to configure and manage ML systems in production.
• See problems first. Build observability dashboards and alerting for model and infrastructure health.
• Test it properly. Write unit and integration tests for ML code, pipelines and deployment workflows.
• Ship securely. Follow security best practice in containerised deployments and data handling.

What you'll bring

• Strong Python, with solid software architecture and development skills
• Real experience deploying and managing ML models in production
• Expertise in cloud platforms, ideally Microsoft Azure, for scalable and reliable ML systems
• Docker and containerisation for deployments that scale and are easy to maintain
• Monitoring, logging and alerting for ML systems and infrastructure
• Version control (Git), package management and dependency tracking
• Data modelling, ETL and databases, both SQL and NoSQL
• A good grasp of core machine learning concepts and algorithms
• A degree in Computer Science, Engineering or a related field (Bachelor's or Master's)

Why you'll like it

• Real scale: your systems power products used by millions of players worldwide.
• Real ownership: you build the platform other engineers and data scientists rely on.
• Real craft: a strong engineering culture with automation, testing and observability built in from the start.
• Real impact: you're the reason models actually make it to production.