Staff AI Scientist

About Uniphore:

Uniphore is The Business AI Company. We enable businesses to rapidly adopt, significantly transform, and immediately unlock value through AI, by providing a platform that allows business users to effortlessly harness agentic AI, tapping into enterprise knowledge that is grounded in their own proprietary data. We enable the Global top 1000 customers achieve their business outcomes through our core principles of providing composable, sovereign and secure AI.

Role Overview

We are seeking an experienced Staff AI Scientist to join our AI Science team, reporting to the Director of AI Science. This role offers the opportunity to work on AI technologies and contribute directly to enterprise use-cases of generative AI systems deployed at global scale.

Key Responsibilities

• Design and develop agentic AI systems and retrieval-augmented generation (RAG) applications for large enterprises.
• Research and implement methods for fine-tuning, alignment, and evaluation of AI agents and RAG systems.
• Contribute to the development of our agentic platform, including context management, memory architectures, evaluation frameworks, and trust and safety mechanisms.
• Conduct applied AI research by evaluating, adapting, and extending state-of-the-art open-source models.
• Lead AI projects from research and prototyping through production deployment as part of a cross-functional teams including engineering, product management, and solutions engineering.

Qualifications

• Master's or Ph.D. in Computer Science, Electrical Engineering, Computer Engineering, or a related field.
• 7+ years of industry experience in AI/ML for candidates with a Master's degree, or 3+ years for Ph.D. candidates.
• Competence in probability, statistics, linear algebra, and machine learning.
• Expertise in Python and deep learning frameworks such as PyTorch or TensorFlow, with experience using modern ML tooling (e.g., Hugging Face, vLLM).
• Experience building production-ready, scalable machine learning systems.
• Deep understanding of modern AI techniques in areas such as NLP, multimodal AI, speech (ASR), or computer vision.
• Strong publication record in top-tier conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, or AAAI is preferred.
• Experience with model fine-tuning and reinforcement learning is a plus