{"id":4015337,"url":"https://alion.io/job/xebia-ml-engineer","title":"ML Engineer","company":{"id":1386,"name":"Xebia","domain":"xebia.com","url":"https://alion.io/company/xebia","size_band":null,"is_staffing_agency":false,"employer_type":"services","is_intermediary":false,"listed_via":null,"ats_vendor":"Recruitee","truth_index":{"grade":"C","score":58,"open_postings":37,"ghost_share":0.703,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":40,"computed_at":"2026-11-10T06:52:35Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Warsaw, Poland","Gdańsk, Poland","Rzeszów, 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":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"GCP","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Snowflake","optional":false},{"name":"SQL","optional":false},{"name":"Dask","optional":true},{"name":"Feature Store","optional":true},{"name":"Google BigQuery","optional":true},{"name":"Recommender Systems","optional":true},{"name":"Spark","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-11-10T10:59:17Z","employer_posted_date":null,"last_verified_at":"2026-11-10T10:59:17Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"Xebia is a global AI-first, digital transformation, and engineering partner. With over 25 years of experience and a team of 5,000 professionals across 16 countries, we help organizations design and build scalable products, platforms, and data-driven solutions. \nWe specialize in Artificial Intelligence, Data and Cloud, Intelligent Automation, and Digital Products, combining deep technical expertise with a strong focus on engineering excellence and a people-first culture. \nIn the CEE region, we’re a team of nearly 1,000 experts delivering modern applications, data platforms, and AI solutions for clients such as Millennium, ING, Play, Edenred, Arabian Drilling, FedEx, Leroy Merlin, Truecaller, Volotea, Schmitz Cargobull, and many, many more. We work with leading technologies including AWS, Azure, GCP, Databricks, and Snowflake, and combine strong engineering culture with a consulting mindset and a continuous focus on growth and knowledge sharing. \n\nZakres obowiązków\nbuilding, deploying and maintaining production-grade machine learning services and specialised tooling for models in the advertising domain,\ndesigning and orchestrating ML engineering workflows, including model packaging, deployment strategies, observability and operational stability,\nproductionising machine learning models in close collaboration with Data Science and Research teams,\ndeveloping and maintaining automated deployment pipelines, model monitoring, alerting and rollback strategies,\nensuring reproducible model packaging and reliable model lifecycle management,\nsupporting production ML systems through troubleshooting, root cause analysis and continuous improvements,\napplying DevOps and Site Reliability Engineering (SRE) principles to improve the reliability, scalability and maintainability of ML services,\nusing AI-assisted coding tools to improve engineering productivity while maintaining high code quality.\nWymagania\nstrong software engineering fundamentals and experience writing production-quality, modular and maintainable code,\nproven experience deploying and operating machine learning models in production environments,\nhands-on experience with automated monitoring, alerting and production troubleshooting,\nadvanced knowledge of MLOps, including model lifecycle automation, CI/CD pipelines and release management,\npractical experience with cloud platforms, preferably Google Cloud Platform (GCP), supporting large-scale data and ML workloads,\nstrong Python and SQL skills in machine learning engineering contexts, including training orchestration and inference support,\nexperience with containerised services and production deployment environments,\nsolid understanding of DevOps principles and SRE practices applied to 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