MLE/SMLE Template
Core
Building and deploying production-grade ML software, tools, and infrastructure to solve critical client challenges across government, finance, retail, energy, life sciences, and defence.
Role type
Senior Machine Learning Engineer (MLOps/Infrastructure)
Builds
Reusable, scalable ML systems and infrastructure
Domain
AI/ML, Cloud Infrastructure, Enterprise Software
Deliverable
production ML models
Required skills
Python, Machine Learning lifecycle, Scikit-learn, TensorFlow, PyTorch, Cloud platforms (AWS, Azure, GCP), Docker, Kubernetes, Software engineering best practices, Probability, Statistics
Preferred skills
Technical scoping, Architectural decisions, Client advisory, Cross-functional collaboration
Technologies
AWS, Azure, GCP, Docker, Kubernetes, Scikit-learn, TensorFlow, PyTorch
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.
