01Key Responsibilities
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- Implement and customise state-of-the-art AI research papers into working prototypes and production-ready models.
- Conduct experiments to improve model performance through fine-tuning, prompt engineering, architecture optimisation, and evaluation.
- Develop and manage training and evaluation datasets, including synthetic data generation, annotation workflows, and benchmark creation.
- Collaborate with engineering and product teams to integrate AI research into scalable production systems.
- Stay current with advancements in AI research and contribute to research planning and technical innovation.
- Document experiments, technical findings, and contribute to patents, technical reports, and reusable research assets.
- Ensure reproducibility, benchmarking, and responsible AI practices across research activities.
Required Skills:-
- Solid expertise in Large Language Models (LLMs), Generative AI, multimodal AI, and computer vision.
- Experience implementing AI research papers from leading research conferences and publications.
- Proficiency in Python and AI/ML frameworks such as PyTorch, TensorFlow, or JAX.
- Experience with Hugging Face Transformers, Diffusers, MLflow, TensorBoard, or similar machine learning tools.
- Knowledge of distributed training frameworks such as DeepSpeed, FSDP, or Horovod.
- Experience with model optimisation techniques including quantisation, pruning, and knowledge distillation.
- Familiarity with data management and annotation tools such as DVC, Apache Arrow, or Label Studio.
- Strong understanding of evaluation metrics, benchmarking, experimentation, and reproducible research.
Qualifications & Experience:-
- Master s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
- Experience in AI research, machine learning, deep learning, or advanced AI technology development.
- Demonstrated experience developing AI prototypes, research models, or production-ready AI systems.
- Publications, patents, or contributions to AI research or open-source projects are advantageous.
Preferred Attributes:-
- Strong analytical and problem-solving abilities.
- Research-oriented mindset with attention to experimentation and reproducibility.
- Ability to collaborate effectively across research, engineering, and product teams.
- Excellent technical communication and documentation skills .
