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For Companies At a Glance
- Tasks: Design and optimise AI prompts, architect LLM systems, and deploy scalable GenAI workflows.
- Company: Join a forward-thinking tech company at the forefront of AI innovation.
- Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
- Why this job: Be part of groundbreaking projects that connect people with intelligent systems in impactful ways.
- Qualifications: Strong Python skills and experience with AI/ML technologies and prompt engineering.
- Other info: Dynamic work environment with a focus on creativity and continuous learning.
The predicted salary is between 36000 - 60000 £ per year.
Design and optimize prompts, architect LLM-powered systems and deploy scalable GenAI workflows that connect people and intelligent systems in new, high-impact ways.
THE ROLE
- Prompting & Reasoning Systems
- Design, test and optimize prompts for leading frontier models (GPT-4/5, Claude 3.x, Gemini 2.x, Mistral Large, LLaMA 3, Cohere Command R+, DeepSeek).
- Apply advanced prompting strategies: Chain-of-Thought, ReAct, Tree-of-Thoughts, Graph-of-Thoughts, Program-of-Thoughts, self-reflection loops, debate prompting and multi-agent orchestration (AutoGen/CrewAI).
- Build agentic workflows with tool calling, memory systems, retrieval pipelines and structured reasoning.
- GenAI Application Engineering
- Integrate LLMs into applications using LangChain, LlamaIndex, Haystack, AutoGen and OpenAI's Assistant API patterns.
- Build high-performance RAG pipelines using hybrid search, reranking, embedding optimization, chunking strategies and evaluation harnesses.
- Develop APIs, microservices and serverless workflows for scalable deployment.
- ML/LLM Engineering
- Work with AI+ML pipelines through Azure ML, AWS SageMaker, Vertex AI, Databricks, or Modal/Fly.io for lightweight LLM deployment.
- Utilize vector databases (Pinecone, Weaviate, Milvus, ChromaDB, pgVector) and embedding stores.
- Use AI-powered dev tools (GitHub Copilot, Cursor, Codeium, Aider, Windsurf) to accelerate iteration.
- Implement LLMOps/PromptOps using Weights & Biases, MLflow, LangSmith, LangFuse, PromptLayer, Humanloop, Helicone, Arize Phoenix.
- Benchmark and evaluate LLM systems using Ragas, DeepEval and structured evaluation suites.
- Deployment & Infrastructure
- Containerize and deploy workloads with Docker, Kubernetes, KNative and managed inference endpoints.
- Optimize model performance with quantization, distillation, caching, batching and routing strategies.
EXPERIENCE
- Strong Python skills, with experience using Transformers, LangChain, LlamaIndex and the broader GenAI ecosystem and prompt engineering experience.
- Deep understanding of LLM behaviour, prompt optimization, embeddings, retrieval and data preparation workflows.
- Experience with vector DBs (FAISS, Pinecone, Milvus, Weaviate, ChromaDB).
- Hands-on knowledge of Linux, Bash/Powershell, containers and cloud environments.
- Strong communication skills, creativity and a systems-thinking mindset.
- Curiosity, adaptability and a drive to stay ahead of rapid advancements in GenAI.
BENEFICIAL
- Experience with PromptOps & LLM Observability tools (PromptLayer, LangFuse, Humanloop, Helicone, LangSmith).
- Understanding of Responsible AI, model safety, bias mitigation, evaluation frameworks and governance.
- Background in Computer Science, AI/ML, Engineering, or related fields.
- Experience deploying or fine-tuning open-source LLMs.
TECH STACK
- LLMs: GPT-4/5, Claude 3.x, Gemini 2.x, Mistral Large, LLaMA 3, Cohere Command R+, DeepSeek
- Frameworks: LangChain, LlamaIndex, Haystack, AutoGen, CrewAI
- Tools: GitHub Copilot, Cursor, LangSmith, LangFuse, Weights & Biases, MLflow, Humanloop
- Cloud: Azure ML, AWS SageMaker, Google Vertex AI, Databricks, Modal
- Infra: Python, Docker, Kubernetes, SQL/NoSQL, PyTorch, FastAPI, Redis
AI Prompt Engineering Consultant in London employer: Staffworx
Contact Detail:
Staffworx Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Prompt Engineering Consultant in London
✨Tip Number 1
Get your networking game on! Connect with folks in the AI and tech space, attend meetups, and join online communities. You never know who might have the inside scoop on job openings or can put in a good word for you.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your prompt engineering projects and any cool systems you've built. This is your chance to demonstrate your technical prowess and creativity, so make it shine!
✨Tip Number 3
Don’t just apply anywhere—apply through our website! We love seeing candidates who are genuinely interested in joining us at StudySmarter. Tailor your application to highlight how your skills align with our needs.
✨Tip Number 4
Prepare for interviews by brushing up on your knowledge of LLMs and prompt engineering strategies. Be ready to discuss your thought process and problem-solving skills, as we want to see how you tackle challenges in real-time.
We think you need these skills to ace AI Prompt Engineering Consultant in London
Some tips for your application 🫡
Tailor Your Application: Make sure to customise your CV and cover letter for the AI Prompt Engineering Consultant role. Highlight your experience with LLMs, prompt engineering, and any relevant projects that showcase your skills. We want to see how you fit into our world!
Show Off Your Technical Skills: Don’t hold back on showcasing your technical prowess! Mention your Python skills, experience with frameworks like LangChain or LlamaIndex, and any hands-on work with vector databases. We love seeing candidates who can dive deep into the tech side of things.
Be Creative and Clear: When writing your application, let your creativity shine through while keeping it clear and concise. Use examples to illustrate your thought processes and problem-solving abilities. We appreciate a good narrative that shows us how you think!
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 the role. Plus, it’s super easy – just a few clicks and you’re done!
How to prepare for a job interview at Staffworx
✨Know Your Tech Stack
Familiarise yourself with the specific tools and frameworks mentioned in the job description, like LangChain and Docker. Be ready to discuss your hands-on experience with these technologies and how you've used them in past projects.
✨Master Prompt Engineering Techniques
Brush up on advanced prompting strategies such as Chain-of-Thought and multi-agent orchestration. Prepare examples of how you've applied these techniques to solve real-world problems or optimise workflows.
✨Showcase Your Problem-Solving Skills
Be prepared to tackle hypothetical scenarios during the interview. Think about how you would design a prompt or integrate an LLM into an application, and articulate your thought process clearly.
✨Communicate Your Curiosity
Demonstrate your passion for AI and GenAI advancements. Share any recent trends or developments you've been following, and express your eagerness to learn and adapt in this fast-paced field.
