Description

You will build and maintain the infrastructure required to deploy and serve machine learning models at scale.

Responsibilities

• Build and maintain scalable infrastructure for model and pipeline deployment using containerization and orchestration.
• Develop and maintain secure REST APIs to serve multiple machine learning models to various users.
• Design and optimize data pipelines, storage, and processing systems for training and inference.
• Build data and model dashboards to monitor performance and health in production environments.
• Collaborate with data scientists and software engineers to integrate ML models into existing systems.

Required Skills

• 5+ years of hands-on experience in ML operations or ML engineering.
• Bachelor's degree in Computer Science, Data Science, or a related field.
• Experience with AWS platforms, specifically SageMaker, Jumpstart, and Bedrock.
• Experience with Databricks cloud platforms.
• Proficiency in Python, SQL, and data preprocessing.
• Experience with REST API development and AWS Networking Protocols.
• Solid understanding of containerization (Docker) and CI/CD pipelines.
• Strong knowledge of software engineering principles, including version control, code review, and testing.

Preferred Skills

• Experience with AWS Bedrock, LLMs, LLMOps, Computer Vision, or Motion Capture.
• Familiarity with load balancing, EKS (Kubernetes), and NVIDIA Triton.
• Familiarity with the Hugging Face Diffusers Library.

Key Skills

Education

Bachelor's degree in Computer Science, Data Science