{"id":3879602,"url":"https://alion.io/job/stermedia-mlops-engineer-enterprise-ai-platform","title":"MLOps Engineer (Enterprise AI Platform)","company":{"id":28404,"name":"Stermedia","domain":"stermedia.ai","url":"https://alion.io/company/stermedia","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Recruitee","truth_index":{"grade":"B","score":80,"open_postings":5,"ghost_share":0,"stale_share":0.8,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-09T06:03:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Wrocław, Poland"],"countries":["PL"],"hiring_countries":["PL"],"hiring_countries_total":1,"salary":{"min":17000,"max":21000,"currency":"PLN","period":"month","gross":null,"usd_annual":64404},"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Airflow","optional":false},{"name":"Argo Workflows","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"GitLab CI","optional":false},{"name":"Grafana","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Terraform","optional":false},{"name":"Apache Kafka","optional":true},{"name":"ArgoCD","optional":true},{"name":"DVC","optional":true},{"name":"Feast","optional":true},{"name":"Feature Store","optional":true},{"name":"GitOps","optional":true},{"name":"IAM","optional":true},{"name":"KServe","optional":true},{"name":"MLFlow","optional":true},{"name":"ONNX Runtime","optional":true},{"name":"PyTorch","optional":true},{"name":"Scikit-learn","optional":true},{"name":"Spark","optional":true},{"name":"TensorFlow","optional":true},{"name":"Triton Inference Server","optional":true},{"name":"vLLM","optional":true}],"status":"live","first_seen_at":"2026-11-06T13:34:24Z","employer_posted_date":"2026-11-06","last_verified_at":"2026-11-09T13:42:41Z","board_verified":true,"closed_at":null,"days_open":3,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":3},"description":"We are looking for an MLOps Engineer to help build, automate, and scale production-grade AI and Machine Learning solutions.\nYou will work at the intersection of Machine Learning, Cloud Engineering, and DevOps, helping transform ML models into reliable, scalable, and production-ready services.\nThis role offers the opportunity to work with modern cloud-native technologies, automate ML workflows, improve model deployment processes, and contribute to the development of enterprise AI infrastructure. You will collaborate with Data Scientists, ML Engineers, and Cloud Engineers to ensure that machine learning systems are efficiently deployed, monitored, and maintained.\nRequirements\n2+ years of hands-on experience in MLOps, DevOps, Cloud Engineering, Machine Learning Engineering, or a related field, including practical exposure to ML deployment or ML workflows.\n\nPython: Good programming skills, including writing clean, maintainable, and testable code.\n\nDocker & Kubernetes: Practical experience with containerization and deploying applications in Kubernetes environments.\n\nCloud Platforms: Hands-on experience with at least one major cloud provider: Azure, AWS, or GCP.\n\nCI/CD: Experience with automated build, test, and deployment pipelines using tools such as GitLab CI, GitHub Actions, or similar solutions.\n\nMLOps Fundamentals: Understanding of ML workflows, model deployment, experiment tracking, and model lifecycle management.\n\nWorkflow Orchestration: Familiarity with at least one orchestration tool, such as Kubeflow Pipelines, Apache Airflow, or Argo Workflows.\n\nInfrastructure as Code: Practical knowledge of Terraform or similar IaC tools.\n\nMonitoring: Familiarity with monitoring and logging tools such as Prometheus, Grafana, or equivalent solutions.\n\nEnglish: B2+ level, sufficient for technical discussions, documentation, and everyday collaboration in an international environment.\n\nNice to have:\nExperience with MLflow, including experiment tracking and model registry.\n\nFamiliarity with ML frameworks such as PyTorch, TensorFlow, or Scikit-learn.\n\nExperience with model serving technologies such as KServe, NVIDIA Triton Inference Server, vLLM, or ONNX Runtime.\n\nKnowledge of GitOps and deployment automation using Argo CD or Argo Rollouts.\n\nExperience with data drift detection, model monitoring, or ML observability tools.\n\nFamiliarity with Apache Kafka, Apache Spark, or distributed data processing.\n\nKnowledge of GPU-based ML workloads, distributed training, or inference optimization.\n\nExperience with feature stores (e.g., Feast) or data versioning tools (e.g., DVC).\n\nUnderstanding of cloud security, IAM/RBAC, and secrets management.\n\nSalary:\n17 000 - 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