{"id":4061501,"url":"https://alion.io/job/dollar-general-machine-learning-engineer","title":"Machine Learning Engineer","company":{"id":1759747,"name":"Dollar General","domain":"dollargeneral.com","url":"https://alion.io/company/dollargeneral-com","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Oracle","truth_index":null},"role":"AI/ML","role_family":"AI/ML","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":["Goodlettsville, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"Bash","optional":false},{"name":"Databricks","optional":false},{"name":"Docker","optional":false},{"name":"Kubernetes","optional":false},{"name":"Machine Learning","optional":false},{"name":"MLFlow","optional":false}],"status":"live","first_seen_at":"2026-10-28T14:07:23Z","employer_posted_date":"2026-10-28","last_verified_at":"2026-11-11T08:47:44Z","board_verified":true,"closed_at":null,"days_open":13,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":13},"description":"The Machine Learning Engineer is responsible for optimizing and productionalizing machine learning systems to support scalable, low-latency business solutions. This role evaluates existing machine learning processes, performs statistical analysis to resolve data issues, and improves the accuracy and reliability of predictive automation ML Engineer helps increase the speed of delivery, improve the quality of code, and streamline processes in support of productionalizing models. The ML Engineer is responsible for identifying a production method to establish model code automation to be deployed to production systems and are operational with low latency. He or she is responsible for infrastructure implementation tools such as code and support services in multiple clouds, along with metrics and access to information\nJob Responsibility\nAssess existing machine learning pipelines to identify problems and drive process improvement solutions.\nIdentify bottlenecks in coding development and implementation, including code packaging, to reduce data science and engineering effort.\nParticipate in planning initiatives related to delivery timelines, code quality, and process efficiency improvements.\nExecute improvement plans by defining coding standards and automating development and deployment processes across the organization.\nPerform daily operational tasks including setting up development and production environments, monitoring metrics, and responding to incidents.\nDevelop and maintain expertise in platform configuration management and troubleshooting.\nDeploy application components across appropriate environments using supported infrastructure and cloud technologies.\nCollaborate with Data Scientists, Data Engineers, and technical stakeholders to understand challenges related to machine learning coding and development processes. Translate business objectives into production-ready models and establish metrics to track performance and outcomes.\nJob Qualifications\nExcellent coding and scripting skills (Bash, Shell) and experience implementing large-scale cloud architectures using modern cloud-based container platforms (PaaS/SaaS)\nKnowledge in the deployment of ML Pipelines using Azure ML, Data Bricks (MLFlow)\nTest, build, and deploy ML models in Production akin to DEV Ops but specifically\nfor ML code through infrastructure including DatabricksKnowledge of cloud infrastructure (Azure) and management tools for IT components, storage, networking, caching, etc.\nExperience with containerization, Docker, Kubernetes\nEducation/Experience\nBachelor’s degree in Computer Science, Data Science, Mathematics, or a related field required.\nMaster’s degree in Computational Linguistics, Data Analytics, or a related discipline preferred.\nMinimum of four years of experience as a Machine Learning Engineer or equivalent 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