Hybrid ML Engineer — Production-Grade AI

AI Startups UK; Greater London; Hybrid; GBP 70,000 - 110,000; Full time

Benefits offered by this job

Unlimited Annual Leave Policy

Private healthcare and dental

Enhanced parental leave

Family-Friendly Flexibility & Flexible

Hybrid Working

Job summary

Faculty is hiring a Machine Learning Engineer to deliver bespoke AI solutions for diverse clients. You’ll bring ML from lab to production, shaping scalable software architecture and best practices. Collaborate with clients and cross-functional teams to ensure feasible, timely delivery of production-grade ML systems.

You’ll work with cloud platforms, containerized deployments, and modern ML frameworks, communicating complex concepts to non-technical stakeholders while driving high-impact results.

Qualifications

• Deep understanding of the full machine learning lifecycle and experience operationalising models built with frameworks like Scikit-learn, TensorFlow, or PyTorch.
• Strong Python programming and software engineering practices.
• Hands-on experience with cloud platforms and infrastructure (AWS, Azure, GCP) including architecture and security.
• Experience with containers and orchestration tools (Docker, Kubernetes) to build and manage scalable apps.
• Familiarity with core ML concepts, statistics, and common learning techniques.

Responsibilities

• Build and deploy production-grade ML software, tools, and infrastructure.
• Create reusable, scalable solutions that accelerate ML delivery.
• Collaborate with engineers, data scientists, and commercial leads to solve client challenges.
• Lead technical scoping and architectural decisions for project feasibility and impact.
• Define and implement deployment standards for ML at scale.
• Act as a technical advisor to customers and translate ML concepts for stakeholders.

Skills

Python

ML frameworks

Statistical knowledge

Communication

ML lifecycle

Tools

Docker

Kubernetes

Job description

Faculty is hiring a Machine Learning Engineer to deliver bespoke AI solutions for diverse clients. You’ll bring ML from lab to production, shaping scalable software architecture and best practices. Collaborate with clients and cross-functional teams to ensure feasible, timely delivery of production-grade ML systems.

You’ll work with cloud platforms, containerized deployments, and modern ML frameworks, communicating complex concepts to non-technical stakeholders while driving high-impact results.