AI Prompt Engineer

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.

• 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.