Artificial Intelligence Research Leader
5 days ago
About the Role:
Tata Technologies is seeking an experienced Artificial Intelligence Research Leader to join our team. As a key member of our AI research group, you will be responsible for leading and contributing to cutting-edge AI research projects.
About You:
We are looking for a highly skilled and motivated individual with a strong background in AI research. You should have a Master's degree or Ph.D. in Computer Science, Machine Learning, or a related field, as well as 4+ years of experience in AI/ML research and development.
Your Key Responsibilities:
- Lead and contribute to AI research projects, focusing on Generative AI techniques and architectures
- Develop and fine-tune generative models, including GANs, VAEs, and autoregressive models like Transformer-based networks, LLMs, SLMs, etc.
- Collaborate with cross-functional teams to integrate AI solutions into our products and services
- Stay up-to-date with the latest advancements in AI research and industry trends
- Mentor junior researchers and contribute to the growth of our AI research team
What We Offer:
- An estimated salary of $140,000 - $160,000 per year, depending on your level of experience and qualifications
- A dynamic and supportive work environment with opportunities for professional growth and development
- Access to cutting-edge technologies and resources to support your research and projects
- Collaborative and innovative team culture with regular feedback and evaluation sessions
- Comprehensive benefits package, including health insurance, retirement plan, and paid time off
Requirements:
- Master's degree or Ph.D. in Computer Science, Machine Learning, or a related field
- 4+ years of experience in AI/ML research and development
- Strong knowledge of Generative AI techniques and architectures, including GANs, VAEs, and autoregressive models
- Experience with implementing RAG architecture for sophisticated AI applications
- Familiarity with multimodal models, foundation models, and fine-tuning techniques
- Basic knowledge of AI accelerator chips
- Knowledge of MLOps practices to ensure the robustness and reliability of AI systems
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