ML / AI Engineer

You'll work alongside researchers and engineers building state-of-the-art foundation models, scalable AI infrastructure, and intelligent data systems that power the next era of scientific innovation. This is a rare opportunity to contribute to technology that sits at the intersection of Deep Learning, LLMs, Distributed Systems, and Scientific Computing.

Responsibilities:

• Designing and optimising Foundation Model training pipelines.
• Building and fine-tuning Transformer and LLM architectures.
• Developing intelligent data curation, preprocessing, and tokenisation pipelines.
• Implementing evaluation frameworks, benchmarking systems, and model quality metrics.
• Working on RLHF, alignment, and post-training optimisation workflows.
• Building multilingual and scientific datasets for next-generation AI models.
• Collaborating with infrastructure teams on distributed GPU/TPU training systems.
• Improving scalability, performance, and reliability of production AI platforms.

Requirements:

• Strong Python programming skills.
• Solid understanding of Machine Learning and Deep Learning fundamentals.
• Hands-on experience with PyTorch (or equivalent frameworks).
• Good understanding of Transformers, LLMs, and Foundation Models.
• Knowledge of NLP preprocessing and data pipeline design.
• Strong debugging, analytical, and problem-solving abilities.
• Comfortable working in Linux-based development environments.

Bonus Points If You Have Experience With:

• DeepSpeed, FSDP, or Distributed Training.
• RLHF, DPO, Alignment techniques.
• CUDA programming or GPU optimisation.
• PySpark and large-scale data engineering.
• Scientific ML or numerical computing.
• Dataset curation, evaluation, and benchmarking.
• HPC environments and GPU clusters.