SQUARE ONE RESOURCES sp. z o.o.Warszawa, Masovian, Poland
16 days ago
Job description
technologies-expected :
Terraform
Python
GitLab
Kubeflow
Grafana
Helm
about-project :
We are seeking a highly skilled MLOps / Technical Lead to support a complex and evolving Machine Learning Operations environment. The position requires hands-on involvement in technical architecture, security processes, cloud cost optimization (FinOps), and guiding the engineering team in best practices for scalable and secure MLOps infrastructure. The ideal candidate will be responsible for driving technology decisions, conducting technical evaluations, and coordinating the implementation of robust ML solutions in a cloud-native environment.
responsibilities :
Contribute to the design, development, and maintenance of MLOps infrastructure and solutions.
Define and implement architectural standards and best practices.
Prepare and maintain system architecture documentation.
Participate in hands-on engineering tasks related to infrastructure and MLOps pipelines.
Lead the execution of Proofs of Concept (PoCs) for emerging technologies.
Assess technical solutions and make data-driven recommendations for implementation.
Assist the team in decomposing tasks and epics into manageable components.
Identify and manage dependencies across technical initiatives.
Recommend optimal implementation strategies and approaches.
requirements-expected :
Advanced Python programming skills.
Deep experience with cloud platforms (preferably AWS).
Proficient in Docker and Kubernetes (including Helm / Kustomize).
Experience with Infrastructure-as-Code using Terraform or AWS CDK.
Familiarity with CI / CD pipelines (GitLab CI, ArgoCD).
Exposure to observability tools such as the Grafana Stack, ELK, or Datadog.
Practical experience with implementing and managing security processes in a cloud environment.
Hands-on experience in monitoring, optimizing, and reporting cloud infrastructure costs.
Strong understanding of system architecture and design patterns.
Experience in MLOps or adjacent fields involving ML pipeline orchestration, model serving, monitoring, and data processing.