{"id":2128335,"url":"https://alion.io/job/axa-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":2585458,"name":"AXA Partners CEE","domain":"axa-assistance.pl","url":"https://alion.io/company/axa-assistance-pl","size_band":null,"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":null,"employment_type":"contractor","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Warsaw, Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":170,"max":215,"currency":"PLN","period":"hour","gross":null,"usd_annual":110000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"Flask","optional":false},{"name":"GCP","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Great Expectations","optional":false},{"name":"Hyperopt","optional":false},{"name":"Kedro","optional":false},{"name":"Kubernetes","optional":false},{"name":"LangChain","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false},{"name":"Optuna","optional":false},{"name":"Power BI","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"smolagents","optional":false},{"name":"Tableau","optional":false},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-09-23T09:08:15Z","employer_posted_date":null,"last_verified_at":"2026-09-23T09:08:15Z","board_verified":false,"closed_at":null,"days_open":23,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":23},"description":"AXA Avanssur SA Oddział II w PolsceWe are an internal software house operating within the international insurance group AXA. We provide IT solutions for the needs of AXA companies in Europe. We work in English on a daily basis, in close-knit teams, carrying out international development projects.We are launching an MLOps initiative to modernize the development of our actuarial pricing models by integrating best practices in machine learning operations. This project will involve automating model training, deployment, and monitoring processes, ensuring that our actuaries can operate with increased efficiency, reproducibility, and scalability in a production environment.\nWe are developing an AI-powered platform that leverages GenAI to accurately extract, analyze, and categorize information from large volumes of documents. This platform aims to streamline document processing workflows and enhance the speed and precision of data retrieval across various internal use cases.This is how we organize our workThis is how we workin house\nyou have influence on the technological solutions applied\nyou develop the code \"from scratch\"\nyou focus on product development\nagile\nscrum\nDevelopment opportunities we offerconferences in Poland\ndevelopment budget\nindustry-specific e-learning platforms\nintracompany training\nsoft skills training\nsubstantive support from technological leaders\ntechnical knowledge exchange within the company\n\nZakres obowiązków\nCreate continuous integration templates tailored for model development ensuring version control, testing, and reproducibility of our actuarial pricing models and datasets.\nClose work with members of the ML Engineering team and actuaries to audit and optimize the reliability and scalability of the actuaries' model training pipelines.\nDevelop effective monitoring strategies to track the performance, reliability, and efficiency of the system.\nManage the end-to-end operation of the AI platform to guarantee high availability, responsive performance, and secure data handling during document ingestion and processing.\nOversee the integration and management of cloud resources to optimize cost, performance, and compliance with security standards, thereby enabling continuous innovation on the platform.\nWymagania\nBachelor's or Master's degree in Mathematics, Computer Science, Machine Learning, or related field.\nMastery over Data Science frameworks (pandas, pyspark, sklearn and shap) and MLOPS frameworks (MLFlow, Kedro/Airflow, Hyperopt/Optuna and Great Expectations) in Python.\nExperience with building GenAI agentic workflows using Langchain or smolagents.\nBasic familiarity with Dashboarding tools (PowerBI/Tableau).\nStrong understanding of DevOps methodologies (CI/CD) and experience implementing Github Actions (or similar) workflows.\nExperience with serving models with APIs using Flask or FastAPI.\nExperience with cloud platforms (e.g., AWS, Azure, GCP) and containerization (e.g., Docker, Kubernetes).\nExtremely high attention to detail and rigor.\nTech stack\nDegree\nMachine learning\nData science\nMLflow\nAirflow\nPowerBI\nCI/CD\nDevOps\nGitHub\nFlask\nCloud 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