CommonAI C. I. C.
AI
FoundationModelEngineer
“Foundation Model Engineer at CommonAI C. I. C.. Skills: building, training, evaluating, and deploying LLMs or multimodal models end-to-end, model development, data pipelines, system performance, training and fine-tuning LLMs or multimodal models. Design and implement end-to-end LLM training pipelines. Source and, where appropriate, preprocess datasets for training and evaluation”
What You'll Achieve.
accelerate machine learning and inference; scalable deployment; continuous improvement
Industry & Context.
Ability to debug and improve model performance systematically
Links to relevant projects, papers, or GitHub repositories, A brief description of a model/system you trained and deployed end-to-end
What They're Looking For.
Must Have
Proven experience training and fine-tuning LLMs or multimodal models (not just using APIs), Solid understanding of Model evaluation and validation, Solid understanding of Overfitting, bias/variance tradeoffs, Solid understanding of Data quality and feature engineering, Proficiency in Python, Proficiency in ML frameworks (e. g. PyTorch, TensorFlow), Experience building and maintaining ML pipelines in production, Familiarity with GPU usage and optimisation, Ability to debug and improve model performance systematically
Nice to Have
Knowledge of distributed training or large-scale data processing, Experience with MLOps tools (CI/CD for ML, experiment tracking, model versioning), Background in applied research or publishing, Familiarity with retrieval systems, embeddings, or ranking models, Maths or computer science research background with a focus on developing new algorithms or techniques for training and deploying AI models, Experience working in industry in a large organisation or start-up with an emphasis on developing and deploying cutting edge machine learning
What You'll Do.
Design and implement end-to-end LLM training pipelines
preprocess datasets for training and evaluation
Fine-tune and optimise open weight models (LLMs
Build evaluation frameworks and define performance metrics
Develop and maintain data pipelines and training workflows
Analyse training pipelines and optimise them for latency
and feedback loops for continuous improvement
Experiment with modern AI tooling and services to investigate how they can be leveraged
How You'll Work.
Team & Collaboration
collaborative engineering for the safe and responsible development of foundational AI technologies; share resources and knowledge, to codevelop and grow businesses, fast; work across the full AI lifecycle, from experimentation to scalable deployment
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