{"id":3112161,"url":"https://alion.io/job/ford-motor-company-data-scientist-mlops","title":"Data Scientist MLOPs","company":{"id":4094,"name":"Ford Motor Company","domain":"ford.com","url":"https://alion.io/company/ford-motor-company","size_band":"5000+","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":{"grade":"A","score":99,"open_postings":75,"ghost_share":0,"stale_share":0.213,"repost_share":0.053,"time_to_fill_p50_days":3,"computed_at":"2026-11-02T06:13:15Z"}},"role":"Data Science","role_family":"Data Science","seniority":"middle","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Naucalpan, Mexico"],"countries":["MX"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":31000,"max_usd":84000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":1015},"experience_years_min":3,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agile","optional":false},{"name":"Checkmarx","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"ETL/ELT","optional":false},{"name":"GCP","optional":false},{"name":"Git","optional":false},{"name":"GitHub","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google GKE","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"NLP","optional":false},{"name":"PowerShell","optional":false},{"name":"Python","optional":false},{"name":"PyTorch","optional":false},{"name":"Rest API","optional":false},{"name":"Scikit-learn","optional":false},{"name":"SonarQube","optional":false},{"name":"Tekton","optional":false},{"name":"TensorFlow","optional":false},{"name":"Terraform","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-10-22T17:14:15Z","employer_posted_date":"2026-10-22","last_verified_at":"2026-11-03T02:53:41Z","board_verified":true,"closed_at":null,"days_open":11,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":11},"description":"Ford Global Data Insight & Analytics is seeking a Machine Learning DevSecOps Engineer to build and advance its internal Data Science / AI/ML platform. You will deliver MLOps capabilities on Google Cloud Platform (GCP), standardize machine learning development practices, and support the deployment, training, and retraining of production ML models.\nYou will work in a cross-functional team and collaborate directly and continuously with engineers, business partners and product managers, delivering platform/data products.\n Work closely with the Tech Anchor, Product Manager, and Product Owner to deliver an MLOps platform on GCP using Python and other tools for data scientists across the company.\n\nInnovate and standardize machine learning development practices.\n\nExperiment, innovate, and share knowledge with the team.\n\nUse Git for version control and collaborative development.\n\nLead by example through paired programming for cross-training, upskilling, problem-solving, and faster delivery.\n\nApply ML, NLP, GCP, MLOps, and Kubernetes technologies to platform development.\n\nDeploy ML models and algorithms into production, run simulations for algorithm development, and test different scenarios.\n\nDevelop and integrate RESTful APIs to connect systems and services within the ML platform.\n\nAutomate model deployment, training, and retraining using Agile delivery practices and CI/CD/CT (continuous integration, continuous deployment, and continuous training).\n\nCollaborate with software engineers to understand the platform vision, break work into tasks, and resolve complex issues.\n\nGrow technical capabilities and provide guidance to other software engineers on the team.\n\nPartner with software and Data Scientist to tackle challenging AI problems.\n\n Experience: 3+ years with Python and cloud engineering / services, including GCP Vertex AI and related services.\nEducation: Bachelor’s degree in Computer Science, Computer Engineering, or a similar technical discipline. A master’s degree in Computer Science, Machine Learning, or a related field is preferred.\nTechnical Requirements:\nWe are looking for candidates with strong Python and cloud engineering skills, experience in ML workflow orchestration and MLOPs, and the ability to support production ML platforms.\nCore Technical Requirements:\nPython and Cloud Engineering:\n2+ years of experience with Python and cloud engineering / services such as GCP Vertex AI and related services.\nML Workflow Orchestration:\nExperience with ML workflow orchestration tools such as Airflow or Kubeflow.\nMLOPs:\nExperience with MLOPs tools such as Tekton, Terraform, Security tools (SonarQube, FOSSA, Checkmarx), Containarization (Docker).\nDeploying architectures knowledge (batch vs streaming), deploying protocols (Canary, Blue/Green).\nVertex AI\nModel training\nModel deployment\nModel monitoring\nVertex AI Pipelines\nBigQuery\nData storage and analytics\nFeature engineering\nTraining data preparation\nCloud Storage (GCS)\nDataset storage\nModel artifacts\nPipeline outputs\nGKE (Google Kubernetes Engine)\nRunning containerized ML workloads\nKubeflow deployments\nScalable serving environments\nKubeflow / Vertex AI Pipelines\nML workflow orchestration\nTraining and deployment automation\nDataflow\nETL and large-scale data processing\nPub/Sub\nEvent-driven and streaming ML architectures\nScripting:\nExperience with scripting languages such as Bash or PowerShell.\nGit and Collaborative Development:\nProficiency in Git for version control and collaboration.\nHelpful Additional Experience:\nA master’s degree in Computer Science, Machine Learning, or a related field.\nInfrastructure as code using Terraform.\nSoftware craftsmanship practices such as paired programming and test-driven development.\nUnderstanding of MLOps, the machine learning lifecycle, and frameworks such as scikit-learn, TensorFlow, and PyTorch.\nApplying Agile practices to solution delivery.\nWorking with Dockerfiles, container images, GitHub, and CI/CD concepts.\nKnowledge of generative AI technologies and concepts.\nCollaboration:\nWritten and verbal English proficiency is required. 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