Production AI Engineer – LLM Systems, Hybrid London

Raydar; Greater London; Híbrido; GBP 150.000 - 200.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Equity

Health coverage

Dental coverage

Vision coverage

Flexible time off

Parental leave

Retirement plan

Relocation support

Commuter benefits

Descripción de la vacante

Raydar is seeking a highly skilled AI/ML engineer to design, build and deploy production AI applications built on large language models. You will work with enterprise stakeholders to shape technical solutions, plan scalable architectures, and contribute to an internal platform with reusable infrastructure.

Responsibilities include implementing prompt design, agent workflows and tool integration, building evaluation frameworks, and ensuring systems are observable, reliable and secure before

Formación

• Proficient Python and backend engineering skills.
• Experience communicating with clients or executives on technical projects.
• Undergraduate degree in a technical discipline (e.g., software or electrical engineering).

Responsabilidades

• Design, build and deploy production AI apps using LLMs, including prompts, agents, and tool integration.
• Work directly with enterprise stakeholders to translate complex problems into AI solutions.
• Plan scalable architectures balancing performance, reliability and cost.
• Contribute reusable infrastructure, tools and workflows to internal platforms.
• Build evaluation frameworks measuring accuracy, latency, cost, reliability and safety.
• Make systems observable, reliable, secure and maintainable before production.
• Strengthen engineering practices as the platform matures.

Conocimientos

Python

Backend engineering

Customer-facing

Educación

Undergraduate degree in technical discipline

Descripción del empleo

Raydar is seeking a highly skilled AI/ML engineer to design, build and deploy production AI applications built on large language models. You will work with enterprise stakeholders to shape technical solutions, plan scalable architectures, and contribute to an internal platform with reusable infrastructure.

Responsibilities include implementing prompt design, agent workflows and tool integration, building evaluation frameworks, and ensuring systems are observable, reliable and secure before