Position: Permanent
Role: AI NLP Engineer
Experience: 0 -5 years
Must Have:
- Strong programming skills in Python
- Experience with machine learning frameworks such as TensorFlow, PyTorch, or Keras
- Familiarity with document understanding tools such as OCR and text extraction techniques
- Solid background in state-of-the-art generative AI, such as large language and multimodal models for different customer use cases
- Solid understanding of ML/DL techniques, algorithms, and tools with exposure to CNN, RNN (LSTM), transformers (ViT, BERT, BART, GPT/T5, Megatron, LLMs)
Ways to Stand Out from the Crowd:
- Experience with training large deep learning models and inference deployment
- Familiarity with GPU-based technologies like CUDA, CuDNN, and TensorRT
- Background with Dockers and Kubernetes and deploying machine learning models
- Strong analytical and problem-solving abilities, with the capacity to multitask effectively in a dynamic environment
- Show willingness and ability to dig into unfamiliar territories to tackle complex problems through examples in previous work
- Strong communication and organization skills, with a logical approach to problem-solving, good time management, and task prioritization skills
Roles and Responsibilities:
- Apply alignment techniques such as instruction tuning, reinforcement learning from human feedback (RLHF), and parameter-efficient fine-tuning such as p-tuning, adapters, LoRA, and so on to improve use cases
- Develop, train, fine-tune, and deploy LLMs for driving embodied conversational AI systems including multimodal understanding, speech synthesis, image generation, and dialog reasoning
- Study and develop cutting-edge techniques in deep learning, graphs, machine learning, and data analytics, and perform in-depth analysis and optimization to ensure the best possible performance on current- and next-generation GPU architectures
- Measure and benchmark model and application performance and analyze model accuracy and bias and recommend the next course of action and Improvements
- Collaborate and innovate with various teams on new product features, improvements of existing products, and participate in developing and reviewing code, design documents, use case reviews, and test plan reviews
