Generative AI Engineer

Overview We are seeking a highly skilledGenerative AI Engineerwith 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 optimizeLLM fine-tuning pipelinesincluding LoRA, QLoRA, Supervised Fine-Tuning (SFT), and RLHF. Build and maintainRAG (Retrieval-Augmented Generation)systems using frameworks such as LangChain, LlamaIndex, and custom retrieval layers. Develop, integrate, and extend applications usingModel Context Protocol (MCP) . Architect and deployagentic workflowsusing frameworks like OpenAI Swarm, CrewAI, AutoGen, or custom agent systems. Work withgenerative AI architectures , including transformer-based and multimodal models. Implement scalable storage, embedding, and similarity search usingvector databases(Pinecone, Weaviate, Milvus, Chroma). Ensure robustAI 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 withLLM fine-tuning : LoRA, QLoRA, SFT, RLHF. Deep expertise withRAGframeworks and retrieval pipelines (LangChain, LlamaIndex, custom retrieval layers). Practical experience withMCP (Model Context Protocol)for tool integration and orchestration. Proven work withagent frameworks(OpenAI Swarm, CrewAI, AutoGen, or custom agent systems). Solid understanding oftransformer architectures , generative AI models, and multimodal systems. Proficiency withvector DBs : Pinecone, Weaviate, Milvus, Chroma. Strong grounding inAI 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.

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