RRemoteville

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Graduate AI Consultant

Skills
Computer ScienceAdoptionBusinessDesignDevelopmentsPhysicsProgrammes
Role

What the job involves

The main requirements, responsibilities and hiring steps.

Requirements

  • Evidence of AI products automations agents or workflows personally built
  • Hands-on experience with LLMs APIs prompt design structured outputs retrieval tool use or agentic workflows
  • Ability to write code and connect systems together
  • Python or JavaScript experience would be particularly useful
  • Experience experimenting with tools such as Claude ChatGPT Gemini Cursor GitHub Copilot LangChain LlamaIndex n8n Make Zapier or similar platforms
  • Strong understanding of what LLMs can and cannot do including reliability hallucination evaluation and data-security considerations
  • Ability to explain technical ideas clearly without jargon
  • Strong problem-solving instincts and desire to understand the real problem before reaching for a tool
  • Confidence communicating with technical specialists and first-time AI users
  • Bias towards building testing and iterating rather than discussing possibilities
  • High standards intellectual curiosity and willingness to learn quickly
  • Humility to ask good questions accept feedback and change approach when evidence points elsewhere

Nice to have

  • Curious
  • Client-facing
  • Hands-on
  • Practical
  • Adaptable
  • Commercially aware
  • High ownership

Day to day

  • Support delivery of hands-on AI transformation programmes across client cohorts and accounts
  • Work with experienced consultants to understand real client problems and redesign workflows around AI
  • Prototype and build automations agents and AI-enabled processes alongside client teams
  • Test models tools and approaches to identify what is genuinely useful reliable and appropriate in client environments
  • Support practical workshops and hands-on sessions for both technical and non-technical users
  • Coach learners through projects and help them overcome technical blockers and build confidence using AI
  • Document what has been built so clients can understand operate and extend it themselves
  • Measure progress across learner capability workflow adoption time saved quality improvements and business outcomes
  • Contribute experiments reusable components and delivery methods to the internal Centre of Excellence
  • Stay current on frontier AI models agent frameworks automation platforms and emerging techniques

Hiring process

  • Show something you have actually built
  • Demonstrate it and talk through the problem approach tools trade-offs and improvements
  • Explain a technical AI concept to a non-technical stakeholder
  • Explain the same concept to a technical audience