The Role
Productionalize and optimize machine learning systems for scalable, low-latency solutions. Responsibilities include assessing ML pipelines, improving coding and deployment processes, automating model delivery, configuring cloud infrastructure, monitoring metrics, responding to incidents, troubleshooting platforms, and deploying models across environments. The role collaborates with data scientists and engineers to deliver production-ready models and performance metrics.
Summary Generated by Built In
About Us
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
Job Responsibility
• Assess existing machine learning pipelines to identify problems and drive process improvement solutions.
• Identify bottlenecks in coding development and implementation, including code packaging, to reduce data science and engineering effort.
• Participate in planning initiatives related to delivery timelines, code quality, and process efficiency improvements.
• Execute improvement plans by defining coding standards and automating development and deployment processes across the organization.
• Perform daily operational tasks including setting up development and production environments, monitoring metrics, and responding to incidents.
• Develop and maintain expertise in platform configuration management and troubleshooting.
• Deploy application components across appropriate environments using supported infrastructure and cloud technologies.
• Collaborate 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.
Job Qualifications
• Excellent coding and scripting skills (Bash, Shell) and experience implementing large-scale cloud architectures using modern cloud-based container platforms (PaaS/SaaS)
• Knowledge in the deployment of ML Pipelines using Azure ML, Data Bricks (MLFlow)
• Test, build, and deploy ML models in Production akin to DEV Ops but specifically
• for ML code through infrastructure including DatabricksKnowledge of cloud infrastructure (Azure) and management tools for IT components, storage, networking, caching, etc.
• Experience with containerization, Docker, Kubernetes
Education/Experience
• Bachelor’s degree in Computer Science, Data Science, Mathematics, or a related field required.
• Master’s degree in Computational Linguistics, Data Analytics, or a related discipline preferred.
• Minimum of four years of experience as a Machine Learning Engineer or equivalent experience in production ML systems.
Job Responsibility
• Assess existing machine learning pipelines to identify problems and drive process improvement solutions.
• Identify bottlenecks in coding development and implementation, including code packaging, to reduce data science and engineering effort.
• Participate in planning initiatives related to delivery timelines, code quality, and process efficiency improvements.
• Execute improvement plans by defining coding standards and automating development and deployment processes across the organization.
• Perform daily operational tasks including setting up development and production environments, monitoring metrics, and responding to incidents.
• Develop and maintain expertise in platform configuration management and troubleshooting.
• Deploy application components across appropriate environments using supported infrastructure and cloud technologies.
• Collaborate 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.
Job Qualifications
• Excellent coding and scripting skills (Bash, Shell) and experience implementing large-scale cloud architectures using modern cloud-based container platforms (PaaS/SaaS)
• Knowledge in the deployment of ML Pipelines using Azure ML, Data Bricks (MLFlow)
• Test, build, and deploy ML models in Production akin to DEV Ops but specifically
• for ML code through infrastructure including DatabricksKnowledge of cloud infrastructure (Azure) and management tools for IT components, storage, networking, caching, etc.
• Experience with containerization, Docker, Kubernetes
Education/Experience
• Bachelor’s degree in Computer Science, Data Science, Mathematics, or a related field required.
• Master’s degree in Computational Linguistics, Data Analytics, or a related discipline preferred.
• Minimum of four years of experience as a Machine Learning Engineer or equivalent experience in production ML systems.
Work Where You Matter
At Dollar General, our mission is Serving Others! We value each and every one of our employees. Whether you are looking to launch a new career in one of our many convenient Store locations, Distribution Centers, Store Support Center or with our Private Fleet Team, we are proud to provide a wide range of career opportunities. We are not just a retail company; we are a company that values the unique strengths and perspectives that each individual brings. Your difference truly makes a difference at Dollar General. How would you like to Serve? Join the Dollar General Journey and see how your career can thrive.
Company Overview
Dollar General Corporation has been delivering value to shoppers for more than 80 years. Dollar General helps shoppers Save time. Save money. Every day.® by offering products that are frequently used and replenished, such as food, snacks, health and beauty aids, cleaning supplies, basic apparel, housewares and seasonal items at everyday low prices in convenient neighborhood locations. Learn more about Dollar General at www.dollargeneral.com/about-us.html.
Skills Required
- Bachelor's degree in Computer Science, Data Science, Mathematics, or a related field
- At least four years of experience as a Machine Learning Engineer or equivalent production ML systems experience
- Excellent coding and scripting skills using Bash and Shell
- Experience implementing large-scale cloud architectures using cloud-based container platforms
- Knowledge of ML pipeline deployment using Azure ML and Databricks MLflow
- Experience testing, building, and deploying ML models in production using MLOps practices
- Knowledge of Azure cloud infrastructure and IT component management, including storage, networking, and caching
- Experience with Docker and Kubernetes containerization
- Master's degree in Computational Linguistics, Data Analytics, or a related discipline
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The Company
What We Do
pOpshelf is Dollar General’s retail store concept built around an affordable, stress-free shopping experience and rotating, trend-led finds. Its assortment includes seasonal merchandise, housewares and home décor, health and beauty products, cleaning and paper supplies, party goods, candy and snacks, toys, games, electronics, and more. Most items are priced at $5 or less, helping shoppers pick up everyday essentials and special treats.
