Deployed AI Engineer

Enablis

London, UK

18 days ago

Apply on www.adzuna.co.uk

Prepare application

Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Compensation

£66,000.0 - £106,000.0

Working hours

Regular working hours

Job source

www.adzuna.co.uk

Tech stack

Clean Code Principles Artificial Intelligence Amazon Web Services Cloud Computing Computer Programming Continuous Integration Information Engineering Python (Programming Language) Performance Tuning Software Engineering TypeScript Google Cloud

+6 more

Large Language Models Prompt Engineering Backend AI Platforms Free and Open-Source Software Front End Software Development

Job description

Build working AI proofs on real client data during assess engagements
Deliver client POCs and MVPs, including RAG pipelines, agent architectures, LLM integrations and protocol-driven tooling
Iterate in short loops by demoing frequently, taking feedback and adapting quickly
Engineer for production from day one, including guardrails, security, scalability, telemetry and integration into the client environment
Prove trustworthiness through evaluation practices such as golden datasets, automated evaluation pipelines, accuracy and drift monitoring
Work with client data, including pipelines, messy edge cases and complex integrations
Own and evolve our internal AI platform and codify repeatable field patterns into reusable assets
Lead advanced technical sessions in our upskilling programme and support client engineers during transform engagements
Evaluate emerging AI tools, frameworks and protocols and make pragmatic adoption decisions

Technologies:

AI
AWS
CI/CD
Cloud
Fine-tuning
Support
LLM
MCP
Python
RAG
Security
TypeScript
CTO

Requirements

Strong software engineering foundations, including clean code, testing, CI/CD and a production mindset, with strong general-purpose programming skills such as Python or TypeScript and proficiency in 2+ modern languages
Full-stack capability across front-end, back-end and data engineering
Hands-on production LLM and agent experience, including prompt engineering, agent workflows, RAG, tool orchestration and MCP, with evidence of shipping AI products that users rely on
Evaluation-driven habits, including golden datasets, eval frameworks and guardrails
Client-facing delivery experience and comfort working within enterprise constraints such as security, compliance and legacy integration
High agency and comfort with ambiguity, with the ability to operate with minimal supervision in a client environment
A strong value instinct, with the ability to explain business value and identify when something is not worth building
Experience with vector databases, embeddings and fine-tuning is desirable
Cloud experience, especially AWS or Google Cloud, is desirable
Former founder or startup experience is desirable
Open-source contributions or visible AI side projects are desirable