Senior ML Platform Engineer

Responsibilities

• Own or contribute to technical designs for the feature platform, training platform, serving platform, and underlying operational infrastructure that provides incremental delivery and impact.
• Develop, maintain, and enhance reusable frameworks for AI/ML model development and deployment while establishing and driving best practices in machine learning engineering and MLOps.
• Design, advocate, and implement for availability, scalability, operational excellence, and cost management while delivering incrementally.
• Collaborate closely with ML Engineers, Data Scientists, and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process.
• Mentor and educate ML Engineers and Data Scientists on current and up and coming tools and technologies for ML operations through presentations and documentation.
• Help design and architect an AI platform that adheres to the principles of responsible AI and simplifies privacy compliance.
• Lead build vs buy discussions on technologies that would underpin the feature, training, and serving layers.
• Perform other job-related duties as assigned.

What We’re Looking For

• • 3+ years of experience, depending on education, as an ML, backend, data, or platform engineer developing and working with large scale, complex systems.
  • 2+ years of experience working on a cloud environment such as GCP, AWS, Azure, and with dev-ops tooling such as Kubernetes
  • 1+ year of experience leading projects with at least 1 other team member through completion.
  • 1+ year of experience for Senior designing and developing online and production grade ML systems.
  • A degree in computer science, engineering, or a related field.

• Strong programming skills: Proficiency in languages like Python, Go, or Java.
• System design & architecture: Ability to design scalable and efficient ML systems.
• Cloud platform proficiency: The ability to utilize cloud environments such as GCP, AWS, or Azure.
• ML knowledge: A basic understanding of ML algorithms, techniques, and best practices.
• Data engineering knowledge: Skills in handling and managing large datasets including, data cleaning, preprocessing, and storage
• Collaboration and communication skills: The ability to work effectively in a team and communicate complex ideas clearly with individuals from diverse technical and non-technical backgrounds..
• Strong written communication: The ability to communicate complex ideas and technical knowledge through documentation
• Software leadership skills: A track record of leading projects through completion with quantifiable and measurable outcomes.