{"id":4258585,"url":"https://alion.io/job/virtuslab-mlops-engineer-with-python-senior","title":"MLOps Engineer with Python (Senior)","company":{"id":1440,"name":"VirtusLab","domain":"virtuslab.com","url":"https://alion.io/company/virtuslab","size_band":"201-500","is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"contractor","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Kraków, Poland","Kielce, Poland","Wrocław, Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Azure DevOps","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GitHub Actions","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Scikit-learn","optional":false},{"name":"Time Series","optional":false},{"name":"AWS","optional":true},{"name":"CI/CD","optional":true},{"name":"Docker","optional":true},{"name":"GCP","optional":true},{"name":"Git","optional":true},{"name":"Kubernetes","optional":true},{"name":"NumPy","optional":true},{"name":"pySpark","optional":true},{"name":"Spark","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-11-13T13:12:27Z","employer_posted_date":null,"last_verified_at":"2026-11-13T13:12:27Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"VirtusLab is a leading European software consulting and engineering company. Our mission is to craft clean code and practical solutions with precision and purpose. We foster a dynamic culture rooted in strong engineering, a sense of ownership, and transparency, empowering professionals to make a substantial impact in the software industry.\nAbout the role\nAn opportunity to drive business innovation through robust, production-ready machine learning pipelines. The main focus involves deploying and maintaining ML workflows across Azure cloud infrastructure and on-prem ETL clusters. The role centers on technical collaboration with Data Scientists to build ML-driven forecasting solutions and refine internal engineering standards. The work focuses on classical ML (forecasting, time series, scikit-learn) rather than LLM/GenAI.\n Primarily remote-based engagement with occasional on-site collaboration (approx. once a month).\nPython: Advanced\n\nCloud (prefered Azure): Advanced\n\nCI/CD (GitHub Actions / Azure DevOps): Advanced\n\nIaC: Regular\n\nPyspark: Regular\n\nAirflow: Regular\n\nMLOps: Proven ability to productionize models and set up monitoring: Regular\n\nKubernetes: Nice to have\n\nEnglish: Advanced\n\nView available projects\nProject\nForecasting & Commodities\nProject Scope\nThe ML Engineer / Forecasting & Commodities engagement covers key initiatives supporting critical decision-making processes through the application of Python, PySpark, Kubernetes, and Azure Cloud technology stacks. The scope involves operating within a highly mature technical ecosystem, delivering new features, and scaling solutions to encompass advanced use cases. Key project focus areas include designing and implementing a data science innovation framework while contributing to the organization’s overarching engineering best practices.\nKey Scope of Services\n– Developing libraries, tools, and frameworks to standardise and accelerate the end-to-end development and deployment of machine learning models.\n– Operating within an Azure cloud environment, including developing model training code in AzureML and building/maintaining cloud infrastructure via Infrastructure as Code (IaC).\n– Utilizing distributed data processing tools such as Spark to parallelise machine learning computations across large-scale datasets.\n– Diagnosing and resolving technical issues, ensuring the delivery of high-availability, reusable, and adaptable engineering solutions.\n– Collaborating with cross-functional engineering and data science teams to deliver technical guidance and optimize core workflows.\n– Fostering industry best practices in code quality, security, and scalability.\n– Autonomous, data-driven technical decision-making aimed at driving business growth.\nTech Stack\nPython, PySpark, Airflow, Docker, Kubernetes, Azure (incl. Azure ML), pandas, scikit-learn, numpy, GitHub Actions, Azure DevOps, Terraform, Git @ GitHub\nProject Challenges\n– Building high-accuracy business forecasting systems by delivering reusable toolsets tailored for data scientists and analysts.\n– Streamlining onboarding, deployment, and automated patching pipelines for new ML models.\n– Partnering with cross-functional units to enhance end-customer experience through cutting-edge technology integration.\n– Applying robust DevOps methodologies to establish reproducible patterns across multiple business domains.\nTeam\n3 engineers\n\nWhat we expect in general:\nSolid Python skills, including building production-grade libraries or tools used by other engineers.\n\nRecent hands-on experience taking ML models to production, including deployment and monitoring.\n\nPractical understanding of how Spark workloads scale (partitioning, performance tuning).\n\nHands-on experience building CI/CD pipelines (GitHub Actions or Azure DevOps) and managing cloud infrastructure as code.\n\nCloud experience (Azure preferred, AWS/GCP also welcome) and Docker containerization.\n\nPractical experience with orchestration tools such as Airflow.\n\nSmooth collaboration with cross-functional teams and hands-on involvement in technical design.\n\nClear and direct communication with both tech and business partners.\n\nHigh level of independence, pragmatic problem-solving, and a clear big-picture view.\n\nEnglish at a minimum B2/C1 level.\n\nPrimarily remote-based engagement with occasional on-site collaboration (approx. once a month).\n\nNice to have:\nExperience running workloads, including Spark, on Kubernetes.\n\nA few perks of being with us\nBuilding tech community\n\nFlexible hybrid work model\n\nHome office reimbursement\n\nLanguage lessons\n\nMyBenefit points\n\nPrivate healthcare\n\nTraining Package\n\nVirtusity / in-house training\n\nAccess to the above perks is optional and completely voluntary for B2B contractors\nTech stack\nPython\nCloud\nIaC\nPySpark\nAirflow\nMLOps\nKubernetes\nCI/CD","description_format":"text","description_chars":4796,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":["Home office","Hybrid work"],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["IT Consulting & Digital Transformation","Custom Software Development","Developer Tools"],"lifecycle":[{"event":"open","at":"2026-11-13T14:26:30Z"}],"visa":[],"liveness":{"score":86,"band":"hot","label":"Hiring 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