{"id":33276,"url":"https://alion.io/job/generix-group-senior-devops-engineer","title":"Senior DevOps Engineer","company":{"id":6632,"name":"Generix Group","domain":"generixgroup.com","url":"https://alion.io/company/generix-group","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":null},"role":"DevOps","role_family":"DevOps","seniority":"senior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Cluj-Napoca, Romania"],"countries":["RO"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":28000,"max_usd":79000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":3074},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Datadog","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"Grafana","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Terraform","optional":false},{"name":"Amazon SageMaker","optional":true},{"name":"ArgoCD","optional":true},{"name":"GitLab CI","optional":true},{"name":"Jenkins","optional":true},{"name":"Kubeflow","optional":true},{"name":"MLFlow","optional":true},{"name":"Vertex AI","optional":true}],"status":"live","first_seen_at":"2026-07-10T14:37:12Z","employer_posted_date":"2026-07-10","last_verified_at":"2026-10-16T23:22:13Z","board_verified":true,"closed_at":null,"days_open":98,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":98},"description":"Generix is a leading SaaS vendor specializing in Collaborative Supply Chain solutions that enable the seamless exchange of goods and data across the globe between suppliers and customers, all while responsibly managing their flows. Its platform of innovative digital services optimizes the management of physical flows, by coordinating the entire supply process, from production to delivery, thanks to its WMS, TMS, RMS and VMI solutions; as well as logical and financial flows, by integrating the systems of all players in the chain, from order to payment, with its e-invoicing, e-reporting, EDI, P2P and O2C solutions.\nGenerix creates a distinctive ecosystem designed to cater to its customers, ensuring top-notch performance and sustainability, connecting all global players in retail, industry and services, and fostering the transition toward greater digitalization and energy efficiency. With nearly 850 dedicated employees, Generix provides day-to-day support to over 4,500 companies across more than 60 countries, processes over 500 million invoices, handle more than 40 million order lines each month, and manage 8 million EDI messages daily. Our clientele includes Danone, FM Logistic, Fnac-Darty, Essilor, and Ferrero.\n This role is responsible for designing, implementing, managing, and optimizing the infrastructure, automation pipelines, and workflows that support the entire lifecycle of software development, data processing, Analytics and machine learning model deployment. This individual will be a key technical expert ensuring the reliability, scalability, efficiency, and speed of our development, data, analytics, and ML operations, fostering collaboration between teams and promoting best practices across DevOps, DataOps, and MLOps domains.\nHere's what you'll be doing:\nDesign, build, and maintain robust CI/CD pipelines for software applications, data transformations (ETL/ELT), and machine learning models (training, validation, deployment).\nImplement and manage Infrastructure as Code (IaC) using tools like Terraform to ensure reproducible and scalable environments (cloud or on-premise).\nDevelop and automate data quality checks, data pipeline monitoring, and alerting systems within the DataOps framework.\nEstablish and manage MLOps workflows including experiment tracking, model versioning, automated model retraining, and performance monitoring (drift, bias detection).\nImplement comprehensive monitoring, logging, and alerting solutions across all systems and pipelines (applications, data flows, ML models).\nCollaborate closely with software developers, data engineers, data scientists, and analysts to understand their needs and provide operational support and tooling.\nChampion and enforce best practices in security, reliability, and performance across all operational domains.\nTroubleshoot and resolve complex infrastructure, pipeline, and deployment issues.\nEvaluate and recommend new tools and technologies to improve operational efficiency and capabilities.\nThese objectives are not exhaustive and will evolve according to identified needs and current projects.\n Deep expertise in DevOps, DataOps, and MLOps principles, practices, and tooling.\nMastery of CI/CD pipeline design and implementation for diverse artifacts (code, data, models).\nStrong proficiency in cloud infrastructure management and automation (Azure).\nExpertise in containerization and orchestration (Docker, Kubernetes).\nStrong scripting and automation skills (Python, Bash, etc.).\nExperience with monitoring, logging, and observability tools (e.g., Prometheus, Grafana, ELK Stack, Datadog).\nUnderstanding of data engineering concepts and data pipeline orchestration tools.\nFamiliarity with ML model lifecycle management and associated tooling.\nBachelor’s or master’s degree in computer science, Engineering, or a related field.\n5+ years of experience in technical operations roles, with deep expertise in DevOps and demonstrable, hands-on experience implementing DataOps and MLOps practices.\nProven experience with cloud platforms (mainly Azure, AWS is a plus), containerization (Docker, Kubernetes), CI/CD tools (e.g., Jenkins, GitLab CI, ArgoCD), IaC tools (e.g., Terraform), scripting (Python, Bash), data pipeline orchestration (e.g., Airflow), and ML platforms (e.g., MLflow, Kubeflow, SageMaker/Vertex AI).\nEnglish or French Level: fluent\n Access to modern tools and technologies.\nA chance to make a meaningful impact on enterprise product quality.\nOpportunities for career growth and development.\nSoft skills and Technical Trainings, Workshops, Conferences, and attendance of different similar events","description_format":"text","description_chars":4616,"description_truncated":false,"requirements":{"experience_years_min":5,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":{"level":"bachelor","optional":false},"security_clearance":false,"languages":[{"language":"English","level":"All levels","optional":false},{"language":"French","level":"All levels","optional":false}]},"benefits":[],"hiring_locations":[{"name":"Romania","iso":"RO","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["Supply Chain & Logistics Software"],"lifecycle":[{"event":"open","at":"2026-07-10T14:37:12Z"}],"visa":[],"liveness":{"score":25,"band":"fade","label":"Fading","p_open":1,"p_active":0.7,"p_room":0.36,"age_days":97,"expected_fill_days":56,"reasons":["conf:0","win:tail","crowd:"],"computed_at":"2026-10-16T05:45:00Z"},"pay":null,"html_url":"https://alion.io/job/generix-group-senior-devops-engineer","json_url":"https://alion.io/job/generix-group-senior-devops-engineer.json","meta":{"generated_at":"2026-10-16T23:24:58Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per 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