LLM Engineer

We are seeking an experienced LLM Engineer to join our team.
You will be responsible for designing, developing, and optimising solutions leveraging Large Language Models (LLMs) and modern NLP/AI frameworks. You will collaborate with cross-functional teams to build scalable AI applications, integrate open-source tools, and drive innovation in the rapidly evolving field of generative AI.

WHAT WILL I BE DOING?

• Act as a subject matter expert in LLMs, NLP, and AI-driven solutions.

• Design, implement, and optimise pipelines for training, fine-tuning, and deploying LLMs in production.

• Work with open-source tooling such as LangChain, LlamaIndex, and vector or graph databases to augment and enhance LLM capabilities.

• Build and deploy AI agent systems (e.g., Autogen, CrewAI) to solve real-world business problems.

• Leverage Python’s data and ML stack (Pandas, Polars, Spark, Spark-NLP, TensorFlow, PyTorch, SpaCy, Hugging Face Transformers, Regex, etc.) to develop scalable AI solutions.

• Apply strong software engineering practices, including version control, testing, and CI/CD.

• Work with cloud platforms (preferably AWS) to deliver production-ready AI solutions.

• Conduct research and stay up-to-date with advancements in ML, NLP, and generative AI.

• Collaborate with data scientists, engineers, and stakeholders to define requirements and deliver value.

• Produce technical documentation and share knowledge within the team.

WHAT SKILLS AND EXPERIENCES DO I NEED?

• MSc/PhD in Computer Science, AI, or related field (strong plus).

• 3+ years of experience in engineering ML or AI solutions.

• Solid understanding of algorithms, data structures, design patterns, and asynchronous programming.

• Strong knowledge of ML, NLP, and DL concepts, with hands-on experience implementing models.

• Proficiency in Python and its ML/data stack.

• Experience working with transformer models, Hugging Face ecosystem, and NLP frameworks.

• Hands-on experience augmenting LLMs using open-source tools (LangChain, LlamaIndex, vector or graph databases).

• Familiarity with AI agent frameworks (Autogen, CrewAI).

• Experience with cloud platforms (AWS preferred).

• Strong problem-solving, research, and critical thinking skills.

• Ability to quickly grasp new technologies in the evolving AI landscape.

• Solid software engineering practices: version control, testing, CI/CD.