Generative AI Engineer

Overview

We are seeking a highly skilled Generative AI 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 optimize 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, optimising 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, quantification, or model distillation.
• Contributions to open-source LLM or agent-framework ecosystems.
• Familiarity with cloud platforms (AWS, Azure, GCP) and containerization.