{"id":3067736,"url":"https://alion.io/job/virtuslab-python-engineer-with-mlops-senior","title":"Python Engineer with MLOps (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":"direct","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":{"min_usd":64000,"max_usd":106000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":59},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Azure DevOps","optional":true},{"name":"Docker","optional":true},{"name":"Git","optional":true},{"name":"GitHub Actions","optional":true},{"name":"Kubernetes","optional":true},{"name":"NumPy","optional":true},{"name":"pySpark","optional":true},{"name":"Scikit-learn","optional":true},{"name":"Spark","optional":true},{"name":"Terraform","optional":true}],"status":"live","first_seen_at":"2026-10-22T07:13:45Z","employer_posted_date":null,"last_verified_at":"2026-10-22T07:13:45Z","board_verified":false,"closed_at":null,"days_open":7,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":7},"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 AI-driven solutions and refine internal engineering standards.\n\nPython: Advanced\n\nCloud (prefered Azure): Advanced\n\nIaC: Regular\n\nGitHub Actions: Advanced\n\nPyspark: Regular\n\nAirflow: Regular\n\nExperience with observability: Nice to have\n\nMLOps: Proven ability to productionize models and set up monitoring: Nice to have\n\nProductionize 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 with hands-on experience taking projects to production.\n\nPractical experience with orchestration tools such as Airflow.\n\nBackground in distributed data processing, especially Spark.\n\nHands-on experience with Kubernetes.\n\nCloud experience (Azure preferred) and Docker containerization.\n\nSmooth collaboration with cross-functional teams and hands-on involvement in technical design.\n\nClear and direct communication with both tech and business partner.\n\nReadiness to share domain expertise and support other team members in technical growth.\n\nHigh level of independence, pragmatic problem-solving, and a clear big-picture view.\n\nEnglish language proficiency at a minimum [B2/C1] level.\n\nPrimarily remote-based engagement with occasional on-site collaboration (approx. once a month).\n\nA few perks of being with usBuilding tech community\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\nGitHub Actions\nPySpark\nAirflow\nObservability\nMLOps\nKubernetes\nProductionize 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