ML DevOps Engineer in Fusemachines

Fusemachines is a leading AI strategy, talent, and education services, provider. Founded by Sameer Maskey Ph.D., Adjunct Associate Professor at Columbia University, Fusemachines has a core mission of democratizing AI. With a presence in 4 countries (Nepal, the United States, Canada, and the Dominican Republic and more than 250 full-time employees) Fusemachines seeks to bring its global expertise in AI to transform companies around the world.

You will be part of a team deploying state-of-the-art AI solutions for Fusemachines's enterprise clients. For example, suppose Fusumachines data scientists create an innovative solution for automatically reading and processing thousands of documents for one of the world’s largest banks. The solution works brilliantly in a development environment, but how should it be deployed into production? How will end users access the solution? How will it scale to processing millions of documents? What tools or platforms should the client use for monitoring? An ML DevOps engineer at Fusemachines needs to answer these questions AND build out the solution.

Of course, we don’t expect you to know to do this on day 1! You will report to the Director of Data Engineering who will provide you with coaching and guidance as you get up to speed. Most importantly you will need to demonstrate the ability to write solid, production-quality code and enthusiasm for becoming an expert in this exciting new career.

Responsibilities

• Design the data pipelines and engineering infrastructure to support our clients’ enterprise machine learning systems at scale
• Take offline models data scientists build and turn them into a real machine learning production system
• Develop and deploy scalable tools and services for our clients to handle machine learning training and inference
• Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients’ machine learning systems
• Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
• Support model development, with an emphasis on auditability, versioning, and data security
• Facilitate the development and deployment of proof-of-concept machine learning systems
• Communicate with clients to build requirements and track progress

Qualifications

• Experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data Engineer (or equivalent)
• Strong software engineering skills in complex, multi-language systems
• Fluency in Python
• Comfort with Linux administration
• Experience working with cloud computing and database systems
• Experience building custom integrations between cloud-based systems using APIs
• Experience developing and maintaining ML systems built with open source tools
• Experience developing with containers and Kubernetes in cloud computing environments
• Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)
• Ability to translate business needs to technical requirements
• Strong understanding of software testing, benchmarking, and continuous integration
• Exposure to machine learning methodology and best practices
• Exposure to deep learning approaches and modeling frameworks (PyTorch, Tensorflow, Keras, etc.)

Education & Experience

• 2–5 years experience building production-quality software.
• Bachelors or Masters degree and/or equivalent professional experience