{"id":129334,"url":"https://alion.io/job/spyrosoft-senior-mlops-engineer-google-cloud","title":"Senior MLOps Engineer (Google Cloud)","company":{"id":933,"name":"Spyrosoft","domain":"spyro-soft.com","url":"https://alion.io/company/spyrosoft","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Teamtailor","truth_index":{"grade":"B","score":73,"open_postings":95,"ghost_share":0.537,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":15,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"inferred_payroll_markers","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":["PL"],"hiring_countries_total":1,"salary":{"min":140,"max":170,"currency":"PLN","period":"hour","gross":null,"usd_annual":90000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Vertex AI","optional":false},{"name":"Docker","optional":true},{"name":"Git","optional":true}],"status":"live","first_seen_at":"2026-08-27T12:00:39Z","employer_posted_date":null,"last_verified_at":"2026-08-27T12:00:39Z","board_verified":false,"closed_at":null,"days_open":51,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":51},"description":"Project description:\nJoin a team focused on building and scaling enterprise-grade machine learning platforms on Google Cloud. As a Senior MLOps Engineer, you will play a key role in transforming machine learning architectures into reliable, production-ready solutions. Working closely with ML Architects, Data Scientists, and Cloud Engineers, you will design and develop reusable platform capabilities that support the entire ML lifecycle, from model training and validation to deployment, monitoring, and automated retraining.\nYou will contribute to creating robust MLOps standards, improving operational excellence, and enabling teams to deliver machine learning solutions faster, safer, and more efficiently across enterprise environments.\nTech stack:\nGoogle Cloud Platform (GCP)\n\nVertex AI\n\nGemini Enterprise Agent Platform Pipelines\n\nBigQuery\n\nPython\n\nCI/CD\n\nDocker\n\nML Monitoring & Observability\n\nModel Registry & Versioning\n\nGit\n\nRequirements:\nStrong hands-on experience in MLOps, ML Platform Engineering, or Machine Learning Operations\n\nProven production experience with Vertex AI and/or Gemini Enterprise Agent Platform Pipelines\n\nStrong Python software engineering skills\n\nSolid experience with Google Cloud Platform services, especially BigQuery\n\nExperience building modular and reusable ML pipeline components\n\nHands-on experience with CI/CD practices and tools in production environments\n\nStrong understanding of model versioning, monitoring, retraining strategies, and reproducibility\n\nKnowledge of software engineering best practices, testing methodologies, and code quality standards\n\nExperience working closely with Data Scientists and translating experimental models into production-ready solutions\n\nStrong Polish communication skills (minimum B2), both written and verbal\n\nFluent English (C1)\n\nNice to have:\nGoogle Cloud Professional Machine Learning Engineer certification or equivalent\n\nExperience with infrastructure as code and cloud automation tools\n\nKnowledge of cost optimization practices for machine learning workloads\n\nExperience using AI tools in day-to-day workflow\n\nMain responsibilities:\nBuild and maintain production-grade ML workflows using Vertex AI and Gemini Enterprise Agent Platform Pipelines\n\nDesign and develop reusable components for model training, evaluation, registration, deployment, monitoring, and retraining\n\nImplement automated model lifecycle management, including quality controls and approval processes\n\nIntegrate ML pipelines with BigQuery and other Google Cloud services\n\nCollaborate with engineering teams to integrate ML workflows into CI/CD pipelines and multi-environment deployment processes\n\nWork closely with Data Scientists to productionize machine learning models and experimental code\n\nImprove reliability, observability, scalability, and cost efficiency of machine learning workloads\n\nImplement monitoring and alerting mechanisms for model performance and platform health\n\nSupport best practices related to governance, reproducibility, and ML platform standards\n\nContribute to technical design discussions and continuous improvement initiatives within the MLOps 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