AI Engineer Opportunity @ FourKites

4 days ago

Chennai, India Fourkites Full time

About the Role

We are seeking a talented individuals who has recently completed their PhD or Masters to join our cutting-edge team. In this role, you'll tackle complex challenges in large language models (LLMs), optical character recognition (OCR), and model scaling. You'll be at the forefront of developing and optimizing AI systems that push the boundaries of what's possible in machine learning.

Key Responsibilities

  • Lead research initiatives to improve OCR accuracy across diverse document types and languages
  • Train and fine-tune LLMs using domain-specific data to enhance performance in specialized contexts
  • Develop techniques to scale LLMs efficiently for high-volume production environments
  • Design and implement novel approaches to model optimization and evaluation
  • Collaborate with cross-functional teams to integrate AI solutions into production systems
  • Stay current with the latest research and incorporate state-of-the-art techniques
  • Document methodologies, experiments, and findings for both technical and non-technical audiences

Required Qualifications

  • PhD in Computer Science, Machine Learning, AI, or a related field (completed within the last year)
  • Strong understanding of deep learning architectures, particularly transformer-based models
  • Experience with OCR systems and techniques for improving text recognition accuracy
  • Proficiency in Python and deep learning frameworks (PyTorch, TensorFlow, or JAX)
  • Demonstrated ability to implement and adapt research papers into working code
  • Excellent problem-solving skills with a methodical approach to experimentation
  • Strong communication skills to explain complex technical concepts clearly

Preferred Qualifications

  • Research focus during PhD in areas relevant to our work (NLP, computer vision, multimodal learning)
  • Familiarity with distributed training systems for large-scale models
  • Experience with model quantization, pruning, and other efficiency techniques
  • Understanding of evaluation methodologies for assessing model performance
  • Knowledge of MLOps practices and tools for model deployment
  • Publications at top-tier ML conferences (NeurIPS, ICML, ACL, CVPR, etc.)

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