Machine Learning Engineer

Location: Poland

🌍 Who we are:

Adaptiq is a technology hub specializing in building, scaling, and supporting R&D teams for high-end, fast-growing product companies in a wide range of industries.

🧩 About the Product:

Our client is a major online gaming operator building a next-generation transactional platform for user account management. This is a greenfield project designed to operate in a highly regulated, high-volume environment and support millions of concurrent users. The platform will process financial transactions in real time and must ensure high availability, sub-second performance, data integrity, security, and full auditability. One of the key engineering challenges is maintaining reliability and performance during extreme traffic spikes and peak events. 

🌟 About the Role:

We are looking for a Senior MLOps Engineer to design, build, and maintain scalable cloud infrastructure, real-time data pipelines, and production ML workflows. You will work with Infrastructure as Code, event-driven architectures, and the full ML lifecycle, including model deployment, monitoring, data governance, and reliability. You will collaborate closely with Data Engineers and Data Scientists to help design the new system, ensure data and ML workflows work reliably end to end, and understand how changes in data can affect downstream models. The role offers strong technical ownership and the opportunity to influence architecture, technology choices, and long-term system design.

📈 Key Responsibilities:

• Design, deploy, and maintain scalable cloud environments using Infrastructure as Code.
• Build and operate reliable real-time, event-driven data pipelines with fault tolerance and sub-second latency.
• Design and optimize storage backends and data lake infrastructure for both transactional and analytical workloads.
• Implement high-availability networking, API gateways, automated failovers, and zero-downtime routing.
• Integrate data validation, observability, and monitoring into data pipelines to ensure reliability and quickly identify issues.
• Develop and manage infrastructure for ML model training, validation, deployment, and inference.
• Automate the deployment and testing of containerized workloads running on Kubernetes.
• Collaborate with cross-functional teams to define ML governance and reliable end-to-end data flows.
• Drive long-term cloud scaling, FinOps and cost optimization, and disaster recovery planning.
• Provide technical leadership, participate in architecture reviews, and mentor team members on engineering best practices.

💻 Required Competence and Skills:

• 5+ years of experience in cloud infrastructure, data engineering, or distributed systems architecture.
• Proven expertise in infrastructure provisioning tools (e.g., Terraform), containerization (Docker, Kubernetes), and cloud platforms (AWS or GCP).
• Hands-on experience with distributed streaming platforms (Apache Kafka) and change data capture methodologies.
• Strong understanding of object storage architectures (e.g., S3), distributed data lakes, and SQL/NoSQL databases.
• Proficiency in Python and scripting (e.g., Bash) for automation and pipeline orchestration.
• Solid knowledge of ML workflows, data governance principles, and model lifecycle management.
• Ability to translate high-level architectural designs into detailed implementations with performance tuning.
• Excellent communication skills and experience collaborating with data scientists and engineering teams.

➕ Nice to Have:

• Background in fintech, iGaming, or other high-throughput, real-time transactional platforms.

🚀 Why Us:

- We provide 20 days of vacation leave per calendar year (plus official national holidays of a country you are based in).

- We provide full accounting and legal support in all countries we operate.

- We utilize a fully remote work model with a powerful workstation and co-working space in case you need it.