6 дней назад
Deployed AI Engineer (Senior Consultant)
69 000 - 92 000GBP
Мэтч & Сопровод
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Описание вакансии
Текст:
TL;DR
Deployed AI Engineer (Senior Consultant) (AI/LLM): Building client-facing AI prototypes, POCs, MVPs, and production systems with an accent on RAG pipelines, agent architectures, LLM integrations, security, and evaluation. Focus on deploying AI inside client environments, iterating against real feedback, integrating messy data, and evolving reusable internal platform assets.
Location: London, UK (Hybrid)
Salary: £69,000–£92,000 per annum
Company
Enablis delivers complex technology transformation and is building an AI-native consultancy with an internal AI platform and a growing client pipeline.
What you will do
- Build AI proofs of concept on real client data during assessment engagements.
- Deliver client POCs and MVPs using RAG pipelines, agent architectures, LLM integrations, MCP, and tool orchestration.
- Design production-ready systems with guardrails, security, scalability, telemetry, and integration into client environments.
- Create evaluation datasets and automated evaluation pipelines to monitor accuracy and drift.
- Develop data pipelines and integrations for complex client data and edge cases.
- Evolve the internal AI platform and convert field-tested patterns into reusable assets, while leading technical upskilling sessions.
Requirements
- Strong software engineering foundations, including clean code, testing, CI/CD, and a production mindset.
- Hands-on LLM experience, including API integrations, prompt engineering, agent workflows, or RAG systems.
- Proficiency in at least two modern programming languages.
- Awareness of evolving AI tooling such as MCP and A2A.
- Consultancy or client-facing experience, or a strong interest in moving into client work.
Nice to have
- Experience with vector databases, embeddings, or fine-tuning.
- AWS or other cloud experience.
- Open-source contributions or visible AI side projects.
- Experience shipping AI-powered features to production.
Culture & Benefits
- Hands-on work in a small delivery pod alongside technical and delivery leadership.
- Short feedback cycles with prototypes demonstrated every few days.
- Opportunities to contribute to an internal AI platform and reusable engineering accelerators.
- Client and internal upskilling through advanced technical sessions.
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