Generative AI / LLM Engineer

Overview We are seeking a highly skilled Generative AI / LLM Engineer with deep hands-on experience in building, fine-tuning, evaluating, and deploying advanced language-model and agentic systems. The ideal candidate has strong technical expertise across LLM training paradigms, retrieval-augmented pipelines, agent frameworks, and AI safety evaluation. Key Responsibilities Design, implement, and optimise LLM fine-tuning pipelines including LoRA, QLoRA, Supervised Fine-Tuning (SFT), and RLHF. Build and maintain RAG (Retrieval-Augmented Generation) systems using frameworks such as LangChain, LlamaIndex, and custom retrieval layers. Develop, integrate, and extend applications using Model Context Protocol (MCP) . Architect and deploy agentic workflows using frameworks like OpenAI Swarm, CrewAI, AutoGen, or custom agent systems. Work with generative AI architectures , including transformer-based and multimodal models. Implement scalable storage, embedding, and similarity search using vector databases (Pinecone, Weaviate, Milvus, Chroma). Ensure robust AI safety , including red-teaming, adversarial testing, and evaluation of model behavior. Collaborate with cross-functional teams to deliver end-to-end AI-driven features and products. Monitor performance, reliability, and quality of deployed AI systems, optimizing continuously. Required Skills & Experience Strong, hands-on experience with LLM fine-tuning : LoRA, QLoRA, SFT, RLHF. Deep expertise with RAG frameworks and retrieval pipelines (LangChain, LlamaIndex, custom retrieval layers). Practical experience with MCP (Model Context Protocol) for tool integration and orchestration. Proven work with agent frameworks (OpenAI Swarm, CrewAI, AutoGen, or custom agent systems). Solid understanding of transformer architectures , generative AI models, and multimodal systems. Proficiency with vector DBs : Pinecone, Weaviate, Milvus, Chroma. Strong grounding in AI safety , red-teaming strategies, evaluation methodologies, and risk assessment. Experience with Python, distributed systems, and MLOps tooling is a plus. Nice to Have Experience with GPU optimisation, quantisation, or model distillation. Contributions to open-source LLM or agent-framework ecosystems. Familiarity with cloud platforms (AWS, Azure, GCP) and containerisation.

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