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.
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- 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.
