Lead Applied AI Engineer - AI, MLOps & Client Delivery

AI Startups UK; Manchester; Hybrid; GBP 90,000 - 120,000; Full time

Benefits offered by this job

Hybrid working in central Manchester

Healthcare / private medical

Personal development budget

Equity option scheme

Regular socials and events

Job summary

Fuzzy Labs in Manchester is seeking a Lead Applied AI Engineer to guide engineering squads and own client project delivery from discovery to production. You’ll shape technical direction, mentor engineers, and work closely with customers to turn AI opportunities into reliable production systems.

Expect involvement in LLM-powered applications, MLOps, data pipelines, and cloud-native infra, while staying ahead of rapid AI developments and contributing to reusable frameworks and best practices.

Qualifications

• Undergraduate degree in computer science, mathematics or equivalent.
• Strong coding, ML/AI, and production-grade software delivery experience.
• Experience with cloud platforms and DevOps practices.
• Proven track record leading technical delivery and mentoring engineers.
• Excellent communication for client-facing engagements.

Responsibilities

• Lead engineering squads and own technical delivery of client projects.
• Shape technical direction, mentor engineers, and manage delivery risk.
• Work with customers from discovery through to production delivery.
• Design and implement production-grade AI systems and MLOps workflows.
• Lead sprint planning, code reviews, and technical decision-making.

Skills

Python

AI/ML

Leadership

Cloud / DevOps

Client-facing

Production systems

Education

Undergraduate CS/Math or similar

Tools

Git

Unix/Linux

Docker

Open-source AI tools

Job description

Fuzzy Labs in Manchester is seeking a Lead Applied AI Engineer to guide engineering squads and own client project delivery from discovery to production. You’ll shape technical direction, mentor engineers, and work closely with customers to turn AI opportunities into reliable production systems.

Expect involvement in LLM-powered applications, MLOps, data pipelines, and cloud-native infra, while staying ahead of rapid AI developments and contributing to reusable frameworks and best practices.