AI Prompt Engineer

3 days ago

London, United Kingdom Staffworx Limited Full time

AI Prompt EngineerTechnically Sharp & Systems-Minded Youll design and optimize prompts, architect LLM-powered systems and deploy scalable GenAI workflows that connect people and intelligent systems in new, high-impact ways. What Youll Do 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). Buildagentic workflowswith tool calling, memory systems, retrieval pipelines and structured reasoning. GenAI Application Engineering Integrate LLMs into applications usingLangChain,LlamaIndex,Haystack,AutoGen and OpenAIs 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 throughAzure ML,AWS SageMaker,Vertex AI,Databricks, orModal / Fly.iofor lightweight LLM deployment. Utilizevector databases(Pinecone, Weaviate, Milvus, ChromaDB, pgVector) and embedding stores. UseAI-powered dev tools(GitHub Copilot, Cursor, Codeium, Aider, Windsurf) to accelerate iteration. ImplementLLMOps / PromptOpsusing: Weights & Biases,MLflow,LangSmith,LangFuse,PromptLayer,Humanloop,Helicone,Arize Phoenix Benchmark and evaluate LLM systems usingRagas,DeepEval and structured evaluation suites. Deployment & Infrastructure Containerize and deploy workloads withDocker, Kubernetes, KNative and managed inference endpoints. Optimize model performance with quantization, distillation, caching, batching and routing strategies. Youll Bring Strong Python skills, with experience usingTransformers,LangChain,LlamaIndex and the broader GenAI ecosystem. Deep understanding of LLM behavior, 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. Nice to Have Experience withPromptOps & LLM Observabilitytools (PromptLayer, LangFuse, Humanloop, Helicone, LangSmith). Understanding ofResponsible 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, RedisTPBN1_UKTJ

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