ML Ops Engineer
We are looking for an experienced Machine Learning Operations Engineer who has extensive experience working with common Data Engineering platforms and has overseen the end-to-end Machine Learning lifecycle.
The role of an ML Engineer is an intersection of Data Scientist, Data Engineer, and DevOps Engineer. You'll be working in a team of ML engineers that takes on a wide array of responsibilities that encompass building all the infrastructure necessary to take a trained ML Model , integrate and deploy, making it available to other applications.
- Work with teams to design and build cloud hosted, automated pipelines that run, monitor, and retrain ML Models for business applications
- Design and implement Model and Pipeline validation procedures alongside teams of Data Scientists, Data Engineers, and other ML Engineers
- Optimize and refactor development code so that it can be moved to production
- Build ETL Pipelines for new and existing models
- Requisition cloud infrastructure for model and pipeline development environments
- Assemble configurations and specifications to automatically build environments in production
- Create and develop in CI/CD Pipelines which allow for controlled and continuous enhancement of existing work and new features during both development and production phases
- Demo new projects and features to stakeholders and excited team members
- An enthusiasm to ask questions of team members and learn new things is essential, as nobody can be expected to know everything. We pride ourselves in being one of the most supportive teams in the business, and we all build off one another to achieve great things
- University or advanced degree in engineering, computer science, mathematics, or a related field
- 5+ years experience developing and deploying machine learning systems into production
- Hands on experience containerizing code and environments with Docker and/or Kubernetes Experience building ETL Pipelines for ML Model Data Ingestion
- Experience building automated Model Training/Retraining and Validation Pipelines
- Strong experience with AWS ML stack (SageMaker, Lambda, etc.)
- Strong experience with python-based MLOps (data prep, model deployment, monitoring, etc)
- Modeling skills and experience with marketing use cases is a plus
- Experience specifying infrastructure to be built using tools such as Terraform or Jenkins
- Expertise in designing and running unit tests on production code
- Experience with object-oriented/object function scripting languages: Python, Java, C++, Scala, etc.
- Experience working with distributed systems, service oriented architectures and designing APIs
- Opportunity to work on bleeding-edge projects
- Work with a highly motivated and dedicated team
- Competitive salary
- Flexible schedule
- Medical insurance
- Benefits program
- Corporate social events
- Placement and Staffing Agencies need not apply. We do not work with C2C at this time.
Grid Dynamics is a leading provider of technology consulting, agile co-creation, scalable engineering and data science services for Fortune 500 corporations undergoing digital transformation.
We work in close collaboration with our clients on digital transformation initiatives that span strategy consulting, early prototypes and enterprise-scale delivery of new digital platforms. We help organizations become more agile and create innovative digital products and experiences using deep expertise in emerging technology, top global engineering talent, lean software development practices, and high-performance product culture.
Headquartered in Silicon Valley with over 3000 technologists located in engineering delivery centers throughout the US, Central and Eastern Europe, Grid Dynamics has architected and delivered some of the most extensive digital transformation programs in the retail, technology and financial sectors to help its clients win market share, shorten time to market and reduce costs of digital operations on a massive scale.
To learn more about Grid Dynamics, visit www.griddynamics.com, or follow us on Twitter @GridDynamics.
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