Senior LLM Engineer

Requirement:Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.Strong proficiency in Python and ML frameworks such as PyTorch, TensorFlow, Hugging Face Transformers, Lang Chain, or LlamaIndex.Deep understanding of LLM architectures, tokenisation, embeddings, attention mechanisms, and various finetuning strategies (LoRA, PEFT, full finetuning, etc.).Experience with vector databases such as Pinecone, Milvus, or FAISS and hands-on implementation of end-to-end RAG pipelines.Proven experience implementing Graph Databases (e.g., Neo4j, Tiger Graph, Arango DB) and integrating knowledge graphs into AI or LLM systems.Practical experience in developing or modifying AI systems (“Modify AI”), including customising model logic, agent behaviour, reasoning modules, or domain-specific adaptation layers.Strong understanding of NLP, information retrieval, entity-linking/NER, and knowledge-graph-based reasoning is a strong advantage.Experience with cloud-based AI platforms (AWS SageMaker, Azure OpenAI, GCP Vertex AI, etc.) and distributed training environments.Strong problem-solving skills, analytical thinking, and the ability to work in cross-functional teams within fast-paced environments.Job Description:Design, fine-tune, and evaluate Large Language Models (LLMs) such as GPT, Llama, Claude, and Mistral for enterprise-grade or domain-specific applications.Develop custom model architectures, advanced embedding strategies, and robust prompt-engineering frameworks.Implement model-optimisation techniques, including model compression, retrieval-augmented generation (RAG), and multi-agent orchestration.Build scalable training, evaluation, and deployment pipelines for LLM-based applications across cloud and on-premise environments.Integrate LLMs with APIs, enterprise systems, relational databases, NoSQL stores, and knowledge repositories.Collaborate closely with Data Engineers, ML Engineers, Product Managers, and domain experts to translate business challenges into end-to-end AI solutions.Experiment with advanced grounding, alignment, and model-interpretability techniques to ensure reliability and factual consistency.Design and implement knowledge-centric architectures by leveraging knowledge graphs, graph databases, and entity-relation modelling to enhance model reasoning and contextual accuracy.Prototype and enhance “Modified AI” capabilities, such as custom reasoning modules, AI agent behaviours, or domain-adaptive model components.