Generative AI Engineer

The RoleYou'll design, build and deploy generative AI features, from prototype to production, working with product, data, ML and software engineering teams. You'll get strong mentoring and room to grow, whether you're early in your AI career or already have a few years of hands-on experience building with LLMs.What You'll DoBuild LLM-powered applications such as chatbots, copilots, summarisation, search and content generation toolsDesign and implement retrieval-augmented generation (RAG) pipelines using vector databases and embeddingsDevelop prompts, agents and tool-calling workflows, and iterate on them based on evaluation resultsIntegrate foundation model APIs (e.g. OpenAI, Anthropic, Google, open-source models) into production servicesFine-tune or adapt models where appropriate (e.g. LoRA, PEFT, instruction tuning)Build evaluation frameworks to measure quality, accuracy, latency, cost and hallucination ratesDeploy and monitor AI services with attention to scalability, reliability and observabilityImplement guardrails for safety, privacy, bias and security (e.g. prompt injection, data leakage)Write clean, tested, well-documented Python codeCollaborate with product and design teams to translate use cases into working solutionsStay current with fast-moving research, tools and best practices, and share learnings with the teamWhat We're Looking ForEssential0 to 4 years' experience in software engineering, machine learning, data science or a related role (strong projects, internships, open-source work and career changers are welcome)Strong Python skillsHands-on experience building with LLM APIs, such as personal, academic or professional projectsUnderstanding of core generative AI concepts: transformers, embeddings, prompt engineering, context windows, RAG and fine-tuningFamiliarity with REST APIs, Git and basic software engineering practicesSolid grasp of ML fundamentals and model evaluationStrong problem-solving skills and the ability to explain technical ideas clearlyDesirableExperience with frameworks such as LangChain, LlamaIndex, Hugging Face Transformers or PyTorchExperience with vector databases (Pinecone, Weaviate, pgvector, FAISS, Chroma)Familiarity with agent frameworks, tool use and Model Context Protocol (MCP)Experience with cloud platforms (AWS Bedrock, Azure OpenAI, Google Vertex AI) and containers (Docker, Kubernetes)Knowledge of MLOps and LLMOps tools such as MLflow, Weights & Biases, LangSmith or similarExperience with LLM evaluation, red-teaming or responsible AI practicesBachelor's or Master's degree in Computer Science, AI, Maths, Engineering or a related field, or equivalent practical experience