Power Programmer - Distinguished Technologist (EV-Q2-FY-26)

Infosys

Bengaluru

On-site

INR 1,400,000 - 2,200,000

Full time

14 days+

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Job summary

A leading company seeks a passionate Researcher for its Applied AI team to work on cutting-edge AI models. This mid-senior level full-time position offers the opportunity to innovate robust architectures for various applications, requiring proficiency in machine learning and strong academic credentials.

Qualifications

Proven experience in training multi-modal models.
Strong knowledge of transformer architectures.
Experience with model pre-training and finetuning.

Responsibilities

Design and optimize multi-modal AI models for applications.
Innovate scalable architectures to meet future requirements.
Troubleshoot and debug model issues to maintain robustness.

Skills

Machine Learning

Artificial Intelligence

Model Optimization

Transformer Architectures

Education

Master's degree or PhD in Computer Science

Ph.D. in Artificial Intelligence

Job description

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Join to apply for the Power Programmer - Distinguished Technologist (EV-Q2-FY-26) role at Infosys

We are looking for a passionate and talented Researcher to join Infosys Applied AI research team. As an Researcher, you will work on architecting, building, refining, and optimizing state-of-the-art Models that drive cutting-edge multi-modal, multi-domain understanding and generation capabilities. If you have experience building LLM/SLM/Multimodal models, we would love to hear from you.

Design, develop, and train transformer-based models for multiple-modality to support a variety of AI-powered applications. - Experiment with various architectures, training techniques, and optimization methods to improve the model's understanding and generative capabilities. - Innovate robust and scalable architectures to accommodate the future requirements. - Troubleshoot and debug model issues, ensuring the models remain robust and adaptable.

Master's degree or PhD in Computer Science, Artificial Intelligence, Machine Learning, or related fields (Ph.D. preferred). - Proven experience in training models (both text and multi-modal models). - Strong knowledge of transformer architectures and their underlying principles. - Experience with model pre-training, finetuning and distributed training. - One or more scientific publication submissions for conferences, journals, or public repositories (e.g., ICML, ICLR, NeurIPS).

Seniority level