Responsibilities

Contribute to the design, development, and deployment of cutting-edge agentic AI systems across diverse use cases.
Maintain and evolve existing agentic AI infrastructure, ensuring scalability, reliability, and performance
Stay current with the latest research in deep learning, generative AI, and agentic frameworks, and apply insights to practical solutions.
Prototype and iterate rapidly in a fast-paced, agile development environment.
Work independently and manage multiple priorities.
Collaborate cross-functionally with other business units.
Solid background of deep learning and generative AI, including transformers.
Proficiency in Python and familiarity with modern ML frameworks (e.g., PyTorch).
Hands-on experience with fine-tuning small models using Supervised Fine-Tuning (SFT) and Reinforcement Learning (RL).
Understanding of agentic AI concepts and frameworks such as LangChain or similar.
Solid grasp of software engineering principles and version control tools (e.g., Git).

Basic qualifications

Master's degree in Computer Engineering, Computer Science, Electrical Engineering, or related field and 2+ years of Hardware Engineering, Software Engineering, Systems Engineering, or related work experience.
6+ months of academic and/or work experience developing and/or optimizing machine learning models, systems, platforms, or methods.

PhD in Computer Science, Electrical Engineering, or related field
2+ years of AI work experience
Experience with multi-agent systems and planning, reasoning, deep search, agentic RAG for long horizon task automation
Familiarity with multimodal agentic systems (e.g., image, video, text).
Experience with deploying generative AI systems on edge devices or resource-constrained environments.
Comfort with performance optimization and system-level debugging.
Publishing research papers at top-tier AI/ML conferences, e.g., NeurIPS, ICML, and ICLR, as a lead author

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Ai Machine Learning