Senior Machine Learning Platform Engineer

Responsibilities

Own or contribute to technical designs for the feature platform, training platform, serving platform, AI observability platform and GenAI Platform and underlying operational infrastructure to enable Product impact.

Develop, maintain, and enhance frameworks for AI/ML model development and deployment while establishing and driving best practices in MLOps / GenAI engineering.

Design, advocate, and implement for usability, reliability, scalability, operational excellence, and cost management while delivering incrementally.

Collaborate closely with ML Engineers, Data Scientists, Data Engineers and Product Managers to understand their needs and identify opportunities to accelerate the AI/ML development and deployment process.

Mentor and educate ML Engineers, Backend Engineers and Product Managers on current and up and coming tools and technologies for ML operations & GenAI product development through presentations and documentation.

Help design and architect an AI platform that adheres to the principles of responsible AI (Authenticity, Transparency, Equity) and builds privacy compliance into the platform.

Lead build vs buy discussions on technologies that underpin the Platforms we serve.

Participate in on-call and Incident Management processes.

What We're Looking For

4+ 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.