Description :

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 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.