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For Companies At a Glance
- Tasks: Design and deliver cutting-edge AI systems for clients, tackling complex challenges with innovative solutions.
- Company: Join a forward-thinking consulting firm leading the charge in AI engineering.
- Benefits: Remote-first work, competitive salary, and opportunities for professional growth.
- Why this job: Be at the forefront of AI transformation, making a real impact on client success.
- Qualifications: Experience in software or AI engineering, strong Python skills, and client-facing capabilities.
- Other info: Dynamic role with excellent career advancement opportunities in a rapidly evolving field.
The predicted salary is between 48000 - 84000 £ per year.
Senior LLM, RAG & Agentic AI Consulting Engineer - Lead / Senior FDE
Remote First, some trips to client offices and HQ.
Lead the design and delivery of complex, AI-native client engagements, spanning agentic systems, retrieval architectures and semantic layers. This is a senior, hands-on consulting role combining deep technical leadership with strong client presence, shaping both client outcomes and the firm’s long-term AI engineering capability.
As client demand for AI-native transformation accelerates, the consulting practice is expanding its engineering capability across agentic systems, retrieval, ontologies and AI-enabled execution. The Consulting Engineer is a hands-on AI systems builder who combines deep engineering craft with commercial and product thinking to design, build and deploy agentic, retrieval and ontology-based systems for enterprise clients. You will work directly with senior client stakeholders and alongside consulting and orchestration roles, translating complex business challenges into safe, reliable and measurable AI solutions.
Key Accountabilities
- Client-Facing AI Engineering & Agentic System Design: You will design and deliver production-grade AI systems for external clients, including:
- LLM applications using modern orchestration patterns, prompt frameworks and evaluation loops
- Multi-agent architectures, including planning, delegation, safety constraints and monitoring
- Retrieval and vector-based systems, embeddings, structured reasoning and semantic workflows
- Ontology and knowledge modelling literacy to enable precise reasoning and data alignment
- Integrations and automation via APIs, tools and enterprise systems
- Prompt engineering at scale, including pattern libraries, guardrails and explainability checks
- Technical Discovery, Feasibility & Solution Architecture: Working closely with consulting counterparts, you will:
- Translate ambiguous client problems into clear, feasible engineering approaches
- Assess client data, platforms, security constraints and operating models
- Contribute to framing, use-case shaping and technical scoping discussions
- Work directly with client domain experts to surface edge cases and operational realities
- Produce clear, lightweight technical artefacts for client and internal audiences
- Delivery Excellence, AI Ops & Reliability: You will ensure client solutions are safe, observable and enterprise-ready by:
- Implementing evaluation frameworks and safety checks across models and agents
- Designing monitoring, logging, tracing and incident-response patterns
- Applying governance, risk and compliance principles within client environments
- Supporting releases, environments and handover into client operations
- Ensuring reliability, reproducibility, performance and cost controls
Experience & Skills
This is a senior, hands-on consulting engineering role. Candidates should bring:
- Solid experience in software engineering, AI engineering, or applied data engineering
- Strong hands-on experience with LLMs, embeddings, RAG, retrieval stacks and vector databases
- Experience designing or implementing multi-agent systems or tool-calling frameworks
- Strong Python skills with experience building production-grade systems
- Experience working across at least one major cloud AI ecosystem (e.g. Azure/OpenAI, GCP/Vertex, AWS, Anthropic)
- Familiarity with semantic modelling, ontologies, or knowledge graph concepts
- Proven ability to rapidly prototype solutions for client validation
- Experience working directly with clients in consulting or professional services contexts
Staffworx are a UK based Talent & Recruiting Partner, supporting Digital Commerce, Software and Business Consulting sectors across the UK & EMEA.
LLM, RAG & Agentic AI Engineer in City of London employer: Staffworx
Contact Detail:
Staffworx Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land LLM, RAG & Agentic AI Engineer in City of London
✨Tip Number 1
Network like a pro! Reach out to your connections in the AI and engineering space. Attend meetups, webinars, or even local tech events. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Showcase your skills! Create a portfolio that highlights your projects related to LLMs, agentic systems, and retrieval architectures. This will give potential employers a taste of what you can do and set you apart from the crowd.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios related to AI engineering. Be ready to discuss your past experiences and how they relate to the role. Confidence is key, so rehearse until you feel comfortable!
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Keep an eye on our job listings and make sure your application stands out by tailoring it to the specific role.
We think you need these skills to ace LLM, RAG & Agentic AI Engineer in City of London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the role of LLM, RAG & Agentic AI Engineer. Highlight your experience with AI systems, multi-agent architectures, and any relevant projects that showcase your skills in software engineering and client engagement.
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about AI engineering and how your background aligns with our needs. Don’t forget to mention specific experiences that demonstrate your ability to tackle complex client challenges.
Showcase Your Technical Skills: In your application, be sure to highlight your hands-on experience with LLMs, embeddings, and retrieval stacks. Mention any relevant tools or frameworks you've used, especially if they relate to the cloud AI ecosystems we work with.
Apply Through Our Website: We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for this exciting opportunity. Plus, it shows you’re keen on joining our team!
How to prepare for a job interview at Staffworx
✨Know Your Tech Inside Out
Make sure you’re well-versed in the latest AI technologies, especially LLMs, RAG, and multi-agent systems. Brush up on your Python skills and be ready to discuss how you've built production-grade systems in the past.
✨Understand Client Needs
Prepare to demonstrate your ability to translate complex client problems into clear engineering solutions. Think of examples where you've worked directly with clients to shape their requirements and how you’ve tackled ambiguous challenges.
✨Showcase Your Hands-On Experience
Be ready to share specific projects where you’ve designed and delivered AI systems. Highlight your experience with retrieval architectures and semantic workflows, and don’t forget to mention any successful prototypes you’ve developed for client validation.
✨Communicate Clearly and Confidently
Since this role involves a lot of client interaction, practice explaining technical concepts in simple terms. Prepare to discuss how you ensure safety, reliability, and performance in your solutions, as well as how you handle governance and compliance.