Production AI Engineer: LLM Deployments & Impact

Harrington Starr; England; On-site; GBP 90,000 - 120,000; Full time

Job summary

Harrington Starr is seeking a hands‑on AI engineer to design and deploy production‑grade AI applications using the latest LLMs. You will work on scalable solutions that tackle complex business problems and continuously improve model performance through experiments and testing.

Collaborate with users to iterate rapidly while staying current with AI ecosystem developments, and bring strong Python engineering to shipping production code in a fast‑growing AI environment.

Qualifications

• Commercial experience building AI applications with LLMs.
• Strong Python engineering skills and experience shipping production code.
• Experience with RAG, prompt engineering, model evaluation or fine‑tuning.
• Knowledge of modern front‑end technologies is beneficial.
• Strong understanding of machine learning fundamentals.
• Degree in Computer Science, Mathematics or another quantitative discipline (or equivalent industry experience).
• Experience with cloud platforms and modern engineering practices is a plus.

Responsibilities

• Build and deploy production‑grade AI applications using modern LLMs.
• Design scalable AI solutions that solve complex business challenges.
• Evaluate and improve model performance through experimentation and testing.
• Work closely with users to iterate quickly and deliver high‑quality products.
• Keep up to date with the latest developments across the AI ecosystem.

Skills

Python programming

LLMs

Model evaluation

Prompt engineering

RAG

Cloud platforms

Frontend basics

ML fundamentals

Education

Degree in Computer Science, Mathematics or related quantitative discipline

Tools

Cloud platforms

Job description

Harrington Starr is seeking a hands‑on AI engineer to design and deploy production‑grade AI applications using the latest LLMs. You will work on scalable solutions that tackle complex business problems and continuously improve model performance through experiments and testing.

Collaborate with users to iterate rapidly while staying current with AI ecosystem developments, and bring strong Python engineering to shipping production code in a fast‑growing AI environment.