AI Prompt Engineer
hace 5 días
London
AI Prompt Engineering Consultant - Q2-Q3 2026 - future talent Technically Sharp | Systems-Minded | GenAI Focused Design, optimize, and operationalize prompt-driven and agentic AI systems. Architect LLM-powered workflows that connect people, data, and intelligent systems in high-impact, production-ready ways. THE ROLE Prompting, Reasoning & Agentic Systems • Design, test, and optimize prompts for leading frontier models (GPT-4.x/5, Claude 3+, Gemini, LLaMA, DeepSeek, and emerging open-weight models)., • Apply advanced prompting and reasoning techniques, including:, • Chain-of-Thought, ReAct, Tree-of-Thoughts, Graph-of-Thoughts, Program-of-Thoughts, • Self-reflection and critique loops, • Debate prompting and multi-agent collaboration, • Architect agentic workflows using frameworks such as AutoGen, CrewAI, LangGraph, and custom orchestration layers., • Build systems with tool calling, long-term and short-term memory, retrieval pipelines, and structured reasoning constraints. GenAI Application Engineering • Integrate LLMs into real-world applications using LangChain, LlamaIndex, Haystack, AutoGen, and OpenAI Assistant / Responses API patterns., • Design and implement high-performance Retrieval-Augmented Generation (RAG) pipelines, including:, • Hybrid (keyword + vector) search, • Reranking and embedding optimization, • Chunking and document preprocessing strategies, • Evaluation and regression testing harnesses, • Develop APIs, microservices, and serverless GenAI workflows for scalable, secure deployment. ML / LLM Engineering & LLMOps • Work across AI/ML platforms such as Azure ML, AWS SageMaker, Vertex AI, Databricks, Modal, and Fly.io., • Deploy and manage vector databases and embedding stores, including Pinecone, Weaviate, Milvus, FAISS, ChromaDB, and pgVector., • Implement LLMOps / PromptOps practices using tools such as:, • Weights & Biases, MLflow, LangSmith, LangFuse, PromptLayer, Humanloop, Helicone, Arize Phoenix, • Benchmark, evaluate, and monitor LLM systems using RAGAS, DeepEval, custom eval suites, and human-in-the-loop review., • Leverage AI-native developer tools (GitHub Copilot, Cursor, Codeium, Aider, Windsurf) to accelerate iteration and experimentation. Deployment, Performance & Infrastructure • Containerize and deploy GenAI workloads using Docker, Kubernetes, KNative, and managed inference endpoints., • Optimize system performance with:, • Caching, batching, routing, and fallback strategies, • Quantization and distillation for efficient inference, • Cost, latency, and reliability optimization, • Design resilient, observable GenAI systems suitable for production environments. EXPERIENCE • Strong Python engineering skills with hands-on experience across the modern GenAI ecosystem., • Deep understanding of LLM behavior, prompt optimization, embeddings, retrieval strategies, and data preparation workflows., • Practical experience with vector databases and semantic search systems., • Comfortable working in Linux environments with Bash/PowerShell, containers, and cloud infrastructure., • Strong communication skills, creativity, and a systems-thinking mindset., • Curious, adaptable, and motivated to stay ahead of rapid advances in GenAI and AI-native software development. BENEFICIAL • Experience with PromptOps, LLM observability, and evaluation tooling., • Understanding of Responsible AI, safety, bias mitigation, governance, and compliance frameworks., • Background in Computer Science, AI/ML, Engineering, or a related technical discipline., • Experience deploying, fine-tuning, or serving open-source LLMs in production. Staffworx is a UK-based Talent & Recruiting Partner supporting organisations across Digital Commerce, Software Engineering, and Value-Add Consulting sectors throughout the UK & EMEA.
