Description
Faculty is looking for a Machine Learning Engineer to deliver bespoke AI solutions for its diverse clients. The engineer will contribute to scalable software architecture, define best practices, and work with clients and cross-functional teams to ensure technical feasibility and timely delivery of high-quality ML systems.
The ideal candidate will have experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch, strong Python skills, and hands-on experience with cloud platforms and infrastructure.
Responsibilities:
- Building and deploying production-grade ML software, tools, and infrastructure
- Creating reusable, scalable solutions that accelerate the delivery of ML systems
- Collaborating with engineers, data scientists, and commercial leads to solve critical client challenges
- Leading technical scoping and architectural decisions to ensure project feasibility and impact
- Defining and implementing Faculty's standards for deploying machine learning at scale
- Acting as a technical advisor to customers and partners, translating complex ML concepts for stakeholders
Requirements:
- Understanding of the full machine learning lifecycle
- Experience with frameworks like Scikit-learn, TensorFlow, or PyTorch
- Strong Python skills and software engineering best practices
- Hands-on experience with cloud platforms and infrastructure (e.g., AWS, Azure, GCP)
- Experience with container and orchestration tools such as Docker & Kubernetes
- Comfortable with core ML concepts, including probability, statistics, and common learning techniques
- Excellent communication skills
Benefits:
- Opportunity to work on impactful AI projects
- Collaborative and dynamic work environment
- Flexible working arrangements
Interview Process:
- Talent Team Screen (30 minutes)
- Pair Programming Interview (90 minutes)
- System Design Interview (90 minutes)
- Commercial Interview (60 minutes)