MLOPS Engineer with Bigdata

Title: MLOps Engineer

Location: 100% Remote

Duration: Full-Time

Few other details on the roles:

1. Client location: Newtown, PA
2. Both roles needed in EST timezone
3. Remote location is ok for now; if we can get good candidates in the Newtown Square region in Pennsylvania that would be even better
4. Both these roles will be Data & AI Practice FTE roles.

Role 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 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:

• 10+ years experience in Data Engineering space with full stack development, with hands-on experience in building machine learning production infrastructure (MLOps)
• Minimum 5+ year working experience streamlining the development, deployment, and management of machine learning models in production environments.
• Mandatory experience working in Databricks, Azure DevOps, and Client experience specifically in Databricks (Client flow, Feature Store, working w/ the model registry, etc.)
• Experience building end-to-end systems as a Platform Engineer/ Client DevOps Engineer
• Strong software engineering skills in complex, multi-language systems. Fluency in Python, Go or Bash, good understanding of Linux, knowledge of frameworks such as scikit-learn, Keras, PyTorch, Tensorflow, etc
• Experience working with cloud computing and database systems
• Experience building custom integrations between cloud-based systems using APIs.
• Experience on CICD pipelines orchestration experience - deploying machine learning solutions using DevOps principles is quite high
• Experience developing and maintaining Client systems built with open source tools
• Familiarity with one or more data-oriented workflow orchestration frameworks (KubeFlow, Airflow, Argo, etc.)
• Exposure to machine learning methodology and best practices

Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.