Aman Rahman

ML Performance Contractor @ Research Commons | Machine Learning, Deep Learning

Role: Ml Performance Contractor at Research Commons
Location: Guwahati, AS, IN
LinkedIn followers: 500 followers

About Aman Rahman

As an ML Performance Contractor at Research Commons, I contribute to optimizing machine learning performance, leveraging skills in machine learning, deep learning, and artificial intelligence. My work emphasizes in fine-tuning models. Passionate about advancing AI technologies, I aim to continuously explore innovative approaches and contribute meaningfully to the field.

Experience

Ml Performance Contractor
Research Commons
Mar 2026 — Present · Bengaluru, IN

Education

Birla Institute of Technology and Science, Pilani - Goa Campus
Chemical Engineering
2020 — 2024

Contact information

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Work email
•••••@researchcommons.ai
Personal email
•••••@•••••.com
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Current company

Research Commons

Research Services · 7 employees · Founded 2025 · Bengaluru, Ka, India

Research Commons is an enterprise AI research lab focused on building end-to-end, infrastructure-aware systems that enable organizations to develop, train, and deploy AI independently at scale. Our work spans distributed fine-tuning frameworks, post-training pipelines, and high-performance inference systems designed for real-world production environments. We have developed modular stacks that support large-scale model adaptation through techniques such as SFT, preference optimization (including DPO/ORPO), and reinforcement learning-based training frameworks. A key focus has been on inference engine optimization—improving latency, throughput, and hardware utilization through techniques like KV caching strategies, batching optimization, speculative decoding, and GPU-level profiling. This systems-first approach ensures that AI models are not just accurate, but also efficient, scalable, and deployable across diverse enterprise and neo-cloud infrastructures. Beyond core infrastructure, Research Commons operates across multiple applied and foundational research domains. In medical AI, we have worked on cognitive and neurological modeling, advanced image segmentation pipelines, and MedSAM-based systems, alongside reinforcement learning approaches tailored for clinical and imaging workflows. In parallel, we are building foundational math-driven platforms to strengthen the theoretical and educational layer of AI systems, enabling deeper understanding and reproducibility of models. Our work in reinforcement learning extends to designing training environments, control-policy optimization (CPOP-style frameworks), and agent evaluation systems that bridge real-world workflows with scalable training pipelines. Collectively, our efforts—from enterprise AI infrastructure and distributed systems to domain-specific research and foundational math tooling—are aimed at creating a unified ecosystem where organizations can own the full lifecycle of AI, from data and training to deployment

researchcommons.ai

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About Aman Rahman

Experience

Ml Performance Contractor

Education

Birla Institute of Technology and Science, Pilani - Goa Campus

Find verified contacts for anyone on LinkedIn