Generative AI / LLM Engineer

Overview

Generative AI / LLM Engineer

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.