Key Responsibilities:
- Design, develop, and optimize deep learning models for use cases in NLP, computer vision, audio, video, and speech recognition.
- Work with large, multimodal datasets and apply best practices in data preprocessing, feature extraction, and augmentation.
- Collaborate with product managers, engineers, and fellow data scientists to integrate intelligent solutions into production-grade systems.
- Leverage TensorFlow, PyTorch, and other AI/ML libraries to build scalable and efficient models.
- Apply techniques such as transformers, convolutional neural networks (CNNs), recurrent neural networks (RNNs), and self-supervised learning for diverse tasks.
- Conduct performance tuning, model validation, and A/B testing to ensure high accuracyand real-world applicability.
- Participate in cross-functional brainstorming and bring innovative ideas from research to deployment.
- Use Python to write modular, testable, and maintainable code while adhering to development best practices.
Required Skills and Qualifications:
- 2-5 years of experience in machine learning, deep learning, or applied AI roles.
- Strong programming expertise in Python, with demonstrated experience in TensorFlow or PyTorch.
- Proven track record in building and deploying models for natural language processing, computer vision, audio/video processing, and speech recognition.
- Solid understanding of deep learning concepts and architectures such as transformers, CNNs, RNNs, autoencoders, etc.
- Experience with training on large datasets, transfer learning, and deploying models into production environments.
- Familiarity with cloud platforms (AWS, Azure, or GCP) and containerization tools such as Docker.
- Comfortable working with Git, designing APIs, and collaborating in cross-disciplinary agile teams.
- Ability to break down complex problems and clearly communicate technical ideas to non-technical stakeholders.
Preferred Skills:
- Experience working with large language models (LLMs) and tools like LangChain or DeepSeek.
- Exposure to audio feature extraction, speech-to-text engines, and multimodal learning techniques.
- Familiarity with MLOps practices, CI/CD pipelines, and automated deployment workflows.
- Background in real-time data processing and AI-driven product development.
- Prior experience in a consulting or product-focused environment.
