Lead Machine Learning Engineer

Faculty; Greater London; On-site; GBP 80,000 - 120,000; Full time

Job summary

Faculty is seeking a Lead Machine Learning Engineer to spearhead innovative AI projects in Greater London. This role involves setting technical directions, designing scalable ML systems, and leading teams to deliver high-quality outcomes. The ideal candidate will have strong Python skills, experience with frameworks like Scikit-learn, TensorFlow, and expertise in cloud solutions such as AWS, Azure, or GCP.

The role emphasizes leadership, innovation, and excellent communication across technical and non-technical stakeholders. Join us to shape the future of AI!

Qualifications

• Technical expertise to solve problems across various projects.
• Strong Python skills with operational experience in ML frameworks.
• Expert in at least one major Cloud Solution Provider.
• Hands-on experience with Docker and Kubernetes.

Responsibilities

• Lead technical direction and delivery of AI projects.
• Manage high-risk projects and drive innovation.
• Set technical direction for complex ML projects.
• Design and maintain reliable ML/software systems.
• Define project problems and oversee delivery.
• Develop shared resources and guide engineering contributions.
• Lead hiring processes and mentor engineers.
• Develop recommendations for adopting new technologies.

Skills

Python skills

Knowledge of Scikit-learn

Experience with TensorFlow

Experience with PyTorch

Expertise in Cloud Solutions (Azure, GCP, AWS)

Containerisation (Docker)

Orchestration (Kubernetes)

Team management

Excellent communication

Job description

Requirements

• You are a technical expert among your peers, capable of going deep on particular topics and demonstrating breadth of knowledge to solve almost any problem
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• You possess strong Python skills and practical experience operationalising models using frameworks like Scikit-learn, TensorFlow, or PyTorch
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• You are an expert in at least one major Cloud Solution Provider (e.g., Azure, GCP, AWS) and have led teams to build full-stack web applications
,
• You have hands-on experience with containerisation tools like Docker and orchestration via Kubernetes
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• You can successfully manage and coach a team of engineers, setting team-wide development goals to improve client delivery
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• You find novel, clever solutions for project delivery and take ownership for successful project outcomes
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• You're an excellent communicator who can proactively help customers achieve their goals and guide both technical teams and non-technical stakeholders

What the job involves

• Join us as a Lead Machine Learning Engineer to spearhead the technical direction and delivery of complex, innovative AI projects. You will act as a technical expert, applying your skills across various projects from AI strategy to client-side deployments, while ensuring architectural decisions are sound and reliable
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• This role demands a balance of deep technical expertise and strong leadership, focusing on driving innovation, fostering team growth, and building reusable solutions across the organisation. If you're ready to manage high-risk projects and deliver practical, innovative outcomes, this is your chance to shape our future
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• Setting the technical direction for complex ML projects, balancing trade-offs, and guiding team priorities
,
• Designing, implementing, and maintaining reliable, scalable ML/software systems and justifying key architectural decisions
,
• Defining project problems, developing roadmaps, and overseeing delivery across multiple work-streams in often ill-defined, high-risk environments
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• Driving the development of shared resources and libraries across the organisation and guiding other engineers in contributing to them
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• Leading hiring processes, making informed selection decisions, and mentoring multiple individuals to foster team growth
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• Proactively developing and executing recommendations for adopting new technologies and changing our ways of working to stay ahead of the competition
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• Acting as a technical expert and coach for customers, accurately estimating large work-streams and defending rationale to stakeholders