MLOps Engineer

Do you have a solid engineering background in DevOps, Platform Engineering, or SRE? Are Docker, Kubernetes, and IaC tools your daily bread—and do you already have hands-on experience or early projects under your belt at the intersection of infrastructure and Machine Learning? Join our team as an MLOps Engineer and play a key role in designing and scaling cutting-edge AI systems! We are looking for an engineer with at least 2–3 years of experience in infrastructure maintenance and automation who wants to leverage their skills in the world of AI/ML. We place strong emphasis on your ability to comfortably manage compute infrastructure (CPU/GPU/TPU) supporting ML workloads. If you want to grow alongside top AI experts, hone your MLOps capabilities, and build products used by global banks—this is the opportunity for you! Key Responsibilities AI Infrastructure: Build and maintain scalable, production-grade environments for training and serving ML models using Docker, Kubernetes, and cloud platforms.ML CI/CD Pipelines: Design and automate the complete machine learning lifecycle—from testing, training, and validation to production deployment.Infrastructure as Code: Define and manage cloud resources (AWS / Azure / GCP) using Terraform or CloudFormation.Monitoring & Observability: Implement systems to monitor model performance, detect data/concept drift, and set up real-time alerting (using tools like Prometheus and Grafana).Versioning & Tooling: Deploy tools for versioning models, experiments, and datasets (e.g., MLflow, DVC).Cross-functional Collaboration: Partner closely with ML Engineers and Data Scientists to define and enforce production best practices. What We Are Looking For DevOps / SRE Core: Minimum 2–3 years of hands-on experience as a DevOps Engineer, Platform Engineer, SRE, or in a similar role.Containerization & Orchestration: Strong, practical expertise in Docker and Kubernetes.IaC & CI/CD: Hands-on experience with Infrastructure as Code (especially Terraform) and building CI/CD pipelines (e.g., GitLab CI, GitHub Actions, Jenkins).Cloud Platforms: Solid knowledge of at least one major public cloud provider (AWS, Azure, or GCP).Scripting & Automation: Proficiency in Python scripting, as well as Bash / HCL.ML/MLOps Awareness: Solid understanding of the ML model lifecycle, hardware awareness (CPU/GPU/TPU resource allocation), and proven initial hands-on project experience in the AI/ML space.Language Skills: Fluent communication in English (minimum B2 level).Soft Skills: Analytical thinking, goal-oriented mindset, strong teamwork capabilities, and efficient priority management. Nice to Have: Experience with model hosting and inference servers (e.g., KServe, vLLM, NVIDIA Triton Inference Server).Hands-on experience with dedicated MLOps platforms (e.g., Kubeflow, MLflow, DVC).Practice with monitoring and logging tools (Prometheus, Grafana, ELK Stack, OpenTelemetry).Higher education degree (preferably in Computer Science or related fields). What We Offer High-Impact Work: Develop advanced AI features for digital products utilized by leaders in FinTech and global banking.Modern Stack: Work with cutting-edge tools and enjoy a safe environment for experimentation and innovation.Growth Among Experts: Access to certifications, training sessions, conferences, and internal workshops—learn directly from senior AI and MLOps professionals.Flexible Work Model: Hybrid arrangement following the onboarding phase (60% office in Kraków/Katowice, 40% remote).Comprehensive Healthcare: Private medical care coverage for you and your family.Culture & Wellbeing: Sports initiatives, integration events, and community activities—we value active lifestyles and a collaborative atmosphere!