Lead AI ML engineer- REMOTE

Lead AI ML engineer - REMOTE

• Familiarity with load balancing, EKS (Kubernetes), & latest serving ML Model Serving Techniques (ex. NVIDIA Triton).
  
• Familiarity with the Hugging Face Diffusers Library
  
• Migrate to client platform solutions (such as AWS Sagemaker) from existing legacy tech stack
  
• Refactor, tune, optimize, containerize, and make ready for production client’s ML solutions in recommendations domain to NIKE platform solutions (such as AWS Sagemaker)
  
• Enable ML models testing, validation and automate testing framework
  
• Collaborate across functional teams (including Consumer Data Sciences, MLOps Core, Platform teams) to deliver ML models in production and enable end to end testing
  
• Excellent analytical and communication skill
  

ML engineer:

Responsibilities:

• Build and maintain scalable infrastructure for machine learning model & pipeline deployment, including containerization & orchestration.
  
• Develop and maintain scalable & secure REST APIs for serving multiple machine learning models to various users.
  
• Collaborate with data scientists and software engineers to ensure seamless integration of ML models into our systems.
  
• Design and optimize data pipelines, data storage, and data processing systems to support the training and inference processes of machine learning models.
  
• Build and maintain data and model dashboards to monitor model performance and health in production environments.
  
• Collaborate with cross-functional teams to identify and address data quality, data governance, and security considerations in the context of ML operations.
  

Requirements:

• Required
  
• Bachelors degree in Computer Science, Data Science, or a related field. A Masters or Ph.D. degree is a plus.
  
• 5+ years of hands-on experience in ML operations, ML engineering, or related roles.
  
• Experience with AWS or Azure cloud platforms, specifically AWS Sagemaker
  
• Experience with REST API development, AWS Networking Protocols
  
• Solid understanding of infrastructure components and technologies, including containerization (e.g., Docker) and CI/CD pipelines
  
• Strong knowledge of software engineering principles and best practices, including version control, code review, and testing.
  
• Excellent problem-solving skills, with the ability to analyze complex issues and provide innovative solutions in a fast-paced environment.
  
• Strong communication and collaboration skills, with the ability to work effectively with cross-functional teams and stakeholders.