• Job Description

ML/DL Resource With a proven track record of deep learning models for commercial products, you bring valuable expertise. Your deep learning experience must be SOTA approaches in Speech. You’ll be comfortable with working end to end, from initial research to production, as such you’ll be comfortable working in production environments and be comfortable leveraging various ML tools. 

• Responsibilities:-
  

• Develop and implement machine learning models and algorithms to solve complex business problems and enhance AI-driven applications.
• Collaborate with cross-functional teams, including data scientists and software engineers, to integrate AI solutions into existing products and services.
• Conduct data analysis, preprocessing, and feature engineering to extract valuable insights from large datasets, improving model performance.
• Stay updated on the latest AI and machine learning advancements, research papers, and tools, incorporating them into AI development projects.
•  Optimize AI models for performance, scalability, and efficiency, leveraging cloud-based resources and distributed computing frameworks.
• Actively participate in code reviews and collaborative problem-solving sessions, contributing to the improvement of AI solutions and development processes.
• Troubleshoot and resolve AI-related issues, ensuring the reliability and accuracy of AI-driven applications.

• Requirements:-

• Bachelor's Degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field, or equivalent hands-on experience in AI development.
• Proven track record as an AI Engineer, with portfolio examples of successful machine learning or AI projects.
• Proficiency in programming languages commonly used in AI, such as Python, or Rand experience with ML frameworks like TensorFlow or PyTorch.
•  Familiarity with data preprocessing, feature engineering, and model evaluation techniques essential for machine learning projects.
• Strong understanding of various machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and neural networks.
• Hands-on experience with Natural Language Processing (NLP), voice processing, Speaker Diarization or other AI subfields.
•  - Solid grasp of mathematical concepts relevant to AI, such as linear algebra, calculus, and statistics.
• Experience with version control systems like Git, enabling effective collaboration and code management.
• Strong analytical and problem-solving skills, crucial for algorithm development and data analysis.
• Exceptional communication skills, enabling effective collaboration with both technical teams and business stakeholders to translate AI capabilities into business value.