BENGALURU · FULLTIME
AI Research Scientist

CareerNet Consulting
Bengaluru · hybrid · Posted 6d ago
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Section · 01
About this role
We are seeking a highly skilled and innovative Research Scientist specializing in Artificial Intelligence to join our R&D team. This role is ideal for someone with deep expertise in implementing and customizing state-of-the-art AI research papers, optimizing large-scale models, and building robust datasets. You will work on real-world applications of generative AI, large language models (LLMs), and computer vision systems, contributing directly to the development of scalable and impactful AI solutions.
Key Responsibilities:
Implement and customize cutting-edge AI research papers into working prototypes and production-ready models.
• Conduct performance improvement experiments on large-scale models, including fine-tuning, prompt engineering, and architecture modifications.
• Develop and manage training and evaluation datasets, including synthetic data generation, annotation pipelines, and benchmark creation.
• Collaborate with engineering and product teams to deploy research outputs into scalable systems.
• Stay up-to-date with the latest advancements in AI and contribute to internal research strategy.
• Document experiments, share insights, and support innovation through patent filings and technical reports.
Required Qualifications
- Ph.D. or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field.
• 5–7 years in applied ML/AI roles
• Proven experience in implementing and customizing AI research papers (e.g., from arXiv, NeurIPS, ICML).
• Hands-on experience with Large Language Models (LLMs): GPT, LLaMA, Mistral, Falcon.
• Experience with Vision Models: ViT, SAM, CLIP.
• Experience with Generative Models: Stable Diffusion, DALL•E, GANs.
• Experience with Multimodal Systems combining vision, language, and audio.
• Proficiency in Python, PyTorch, TensorFlow, JAX.
• Experience with Hugging Face Transformers, Diffusers, OpenAI Gym.
• Experience with Weights & Biases, MLflow, TensorBoard.
• Experience with Distributed training: DeepSpeed, FSDP, Horovod.
• Knowledge of Model optimization: quantization, pruning, distillation.
• Experience with Data tools: DVC, Apache Arrow, Label Studio
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Section · 02
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