Lead AI Engineer

Us Prolific is not just another player in the AI space – we are the architects of the human data infrastructure that's reshaping the landscape of AI development. In a world where foundational AI technologies are increasingly commoditized, it's the quality and diversity of human-generated data that truly differentiates products and models. We've built a unique platform that connects researchers and companies with a global pool of participants, enabling the collection of high‑quality, ethically‑sourced human behavioural data and feedback. This data is the cornerstone of developing more accurate, nuanced, and aligned AI systems. Our mission is to bridge the gap between AI potential and human insight. We believe that the next leap in AI capabilities won't come solely from scaling existing models, but from integrating diverse human perspectives and behaviours into AI development. By providing this crucial human data infrastructure, Prolific is positioning itself at the forefront of the next wave of AI innovation – one that reflects the breath and the best of humanity. The Role The future of AI development relies on a critical, indispensable component : high‑quality human data. Prolific provides the world's largest and most trusted source of this data to the teams pushing the boundaries of AI technology. As an AI Engineer, you'll be building the intelligent systems that power the crucial interface between humans and models. You will be instrumental in shaping how next‑generation AI systems are trained, evaluated, and aligned. This is a unique opportunity to solve novel problems and have a foundational impact on the entire AI ecosystem, working with a world‑class team and the industry's leading AI labs. What You’ll Be Doing Architect & Build : Lead the design and implementation of production‑grade AI systems that enhance how human feedback is collected, validated, and utilised across our platform. Solve Core Challenges : Develop sophisticated systems for fraud detection, quality assurance, and intelligent participant‑to‑task matching using techniques like vector search and advanced profile representation. Innovate with LLMs : Create and deploy AI agents and systems with immediate product impact - from basic automated data tagging, to advanced features like LLM‑based evaluation judges, all the way to experimental ideas such as synthetic personas. Drive Technical Strategy : Provide technical leadership and mentorship on the practical application of AI / LLM techniques, defining best practices for everything from prompt engineering to fine‑tuning and RAG. Ensure Reliability : Implement and own LLM observability and evaluation systems to ensure our AI features are reliable, performant, and continuously improving. Collaborate Cross‑Functionally : Work closely with data scientists, platform engineers, and research teams to build cohesive, end‑to‑end solutions that push the boundaries of human‑AI interaction. Who We’re Looking For Core Requirements A strong track record of designing, building, and shipping AI‑powered features that have solved real business problems. An inquisitive and experimental mindset, with a passion for tackling ambiguous problems that may not have established solutions. Deep practical knowledge of the modern AI engineering stack (e.g., RAG, vector databases, prompt engineering, fine‑tuning techniques, and evaluation frameworks). The ability to thoughtfully balance cutting‑edge research with the practical constraints of production reliability and scalability. Deep expertise in one or both of the following areas Software Engineering & Systems Design : Expertise in Python and experience building scalable services (e.g., using FastAPI, Django). You have a strong command of system design, infrastructure, and CI / CD pipelines, and you're comfortable taking a feature from concept to deployment. Machine Learning Engineering & Research : A comprehensive background in machine learning, with deep experience in ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) and a strong grasp of the underlying theory behind the models and techniques you use. Bonus Points Experience with modern LLM orchestration frameworks (e.g., LangChain, LlamaIndex, DSPy, LangGraph). Experience in agentic system design and tool calling (Adk, A2A, MCP, etc…) Prior work on human‑in‑the‑loop systems, data annotation platforms, or reinforcement learning from human feedback (RLHF). An interest or experience in synthetic data, human‑in‑the‑loop systems, or AI alignment. Experience building robust monitoring and observability for AI / ML systems in production. #J-18808-Ljbffr