{"id":2869565,"url":"https://alion.io/job/datasmart-mid-machine-learning-engineer","title":"Mid Machine Learning Engineer","company":{"id":3807100,"name":"Datasmart","domain":"datasmart.pt","url":"https://alion.io/company/datasmart-2","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":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Lisbon, Portugal"],"countries":["PT"],"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":"Docker","optional":false},{"name":"FastAPI","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Java","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"Pytest","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Scala","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SQL","optional":false},{"name":"TensorFlow","optional":false}],"status":"live","first_seen_at":"2026-10-19T00:00:00Z","employer_posted_date":null,"last_verified_at":"2026-10-19T00:00:00Z","board_verified":false,"closed_at":null,"days_open":10,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":10},"description":"Sobre a vaga\nJoin our team as a Mid Machine Learning Engineer (Hybrid – Lisbon). You will productionize machine learning models developed by Data Scientists and reliably deploy them into production environments, building and maintaining ML-focused CI/CD pipelines supporting model deployment and lifecycle management.\n\nO que vais fazer\n\nProductionize machine learning models developed by Data Scientists and reliably deploy them into production environments\nBuild and maintain ML-focused CI/CD pipelines supporting model deployment and lifecycle management\nMonitor models in production, tracking performance, failures, and key metrics, and implementing alerting where required\nDevelop clean, structured, maintainable, and testable Python code aligned with engineering standards\nCollaborate closely with Data Scientists and Data Engineers on model deployment and data transformation initiatives\nOptimize model performance, response times, and operational efficiency in production environments\nContribute to retraining automation initiatives and the continuous improvement of MLOps practices\nTake ownership of production deployments, pipelines, monitoring, troubleshooting, and operational improvements\n\nO que é pedido\n\n3+ years of professional experience in Machine Learning\nStrong Python skills, including project structuring, reusable and testable code, object-oriented programming, design patterns, and testing with pytest or unittest\nStrong SQL skills for querying and manipulating data\nUnderstanding of MLOps fundamentals, including Git versioning, pull requests, code reviews, and ML-specific versioning practices\nExperience with core ML frameworks such as TensorFlow, PyTorch, and Scikit-learn\nExperience with CI/CD pipelines, using tools such as GitHub Actions or Jenkins\nFamiliarity with AWS, GCP, or Azure for running and integrating ML workloads\nExperience deploying models using Docker and/or Kubernetes, including an understanding of model optimization and compression\nFamiliarity with building model-serving APIs using FastAPI or Flask\nStrong collaboration and communication skills, with the ability to work effectively across Data Science and Engineering teams\nProactive and structured approach to problem-solving, with the ability to handle moderately complex technical challenges\nStrong sense of ownership and accountability for production deliverables, deployments, pipelines, and fixes\nAbility to learn quickly and adapt to evolving technologies, feedback, and MLOps best practices\nEnglish proficiency at a professional level (minimum B2)\nLiving in Portugal and available to work in a hybrid model in Lisbon\n\nValorizado\n\nBasic knowledge of Java or Scala is a plus, particularly for integration with existing systems\n\nO que é oferecido\n\nopportunity to integrate into a solid company\nparticipate in projects of high recognition, nationally and internationally\nappealing technological environment\ncareer progression\n\nResumo organizado por IA a partir do anúncio original. 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