{"id":2007225,"url":"https://alion.io/job/esenca-machine-learning-engineer-data-mlops","title":"Machine Learning Engineer – Data & MLOps","company":{"id":3909441,"name":"Esenca","domain":"esenca.fr","url":"https://alion.io/company/esenca-3","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":"freelance","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Lille, France"],"countries":["FR"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":48000,"max_usd":99000,"period":"year","method":"role_country_seniority_unknown","sample_n":94},"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Apache Kafka","optional":false},{"name":"BigQuery","optional":false},{"name":"dbt","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Terraform","optional":false}],"status":"live","first_seen_at":"2026-10-05T09:23:39Z","employer_posted_date":null,"last_verified_at":"2026-10-05T09:23:39Z","board_verified":false,"closed_at":null,"days_open":7,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":7},"description":"Dans le cadre d’un projet stratégique autour de la recommandation et de l’IA, nous recherchons un Machine Learning Engineer expérimenté pour concevoir, développer et industrialiser des pipelines Data et ML de bout en bout.\nVous intégrerez une équipe pluridisciplinaire composée de Data Scientists, Data Engineers, développeurs, Tech Lead et Product Owner, au sein d’un environnement fortement orienté Data, ML et Cloud.\nVos missionsConcevoir et développer des data pipelines de bout en bout : ingestion, transformation, stockage et exposition des données.\n\nConcevoir et industrialiser des pipelines ML : préparation des données, entraînement, évaluation, publication et inférence.\n\nParticiper à la conception et à l’évolution d’architectures Data / ML sur GCP.\n\nMettre en place les mécanismes de qualité, reproductibilité, monitoring et observabilité des pipelines.\n\nIndustrialiser les modèles ML pour des usages en production, notamment en inférence temps réel.\n\nParticiper à l’évolution du socle DataOps / MLOps et des outils d’orchestration.\n\nOptimiser les solutions existantes en matière de performance, fiabilité, maintenabilité et coûts Cloud.\n\nParticiper au Run et à la résolution des incidents sur les pipelines et services existants.\n\nGarantir la qualité des développements : tests, revue de code, documentation et bonnes pratiques d’ingénierie.\n\nCollaborer avec les équipes Data, ML et Software Engineering afin de partager les bonnes pratiques.\n\nEnvironnement techniqueData Engineering : BigQuery, dbt, Kafka, streaming, data modeling, Terraform.\nML / MLOps : Python, Deep Learning, embeddings, Docker, Kubernetes, Git, ZenML ou outils similaires, ML pipelines, inférence temps réel.\nCloud : GCP, architectures Cloud distribuées.\nProfil recherché\nProfil recherchéExpertise en conception de pipelines ML et Data.\n\nSolide maîtrise de Python et des environnements Data/ML modernes.\n\nBonne compréhension du cycle de vie d’un modèle ML, de l’expérimentation à la production.\n\nConnaissance des architectures Deep Learning et des modèles basés sur les embeddings.\n\nMaîtrise des concepts de data modeling, streaming, qualité et observabilité des données.\n\nCapacité à concevoir des solutions scalables, robustes et maintenables.\n\nSensibilité aux enjeux de performance et d’optimisation des coûts Cloud.\n\nExpérience significative sur GCP.\n\nCapacité à évoluer dans un environnement pluridisciplinaire et agile.\n\nFrançais courant et anglais professionnel 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