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@ayushtiwari5
ML Research Engineer specializing in LLMs, RAG, and production ML.
I am an ML Research Engineer focused on large language models, generative AI, and production-ready ML systems. I specialize in fine-tuning LLMs, building retrieval-augmented generation (RAG) pipelines, and optimizing models for low-latency deployment.
My work includes architecting multi-agent Agentic AI systems, improving inference and orchestration for complex workflows, and applying state-of-the-art techniques across NLP and computer vision. I have hands-on experience with PyTorch, Transformers, diffusion models, and model optimization strategies such as QLoRA and LCM.
I bring measurable impact from internships and industry roles — reducing latency, improving model accuracy, and optimizing cloud costs — and I enjoy tackling challenging research-to-production problems that blend deep learning research with scalable engineering.
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Work history, roles, and key accomplishments
Gupshup
Mar 2024 - Present (1 year 8 months)
Optimized legacy read-prediction code reducing latency by 80% and architected/deployed multi-agent Agentic AI systems with dynamic routing and context sharing for complex RAG workflows.
OpenAI
Sep 2022 - Nov 2022 (2 months)
Improved medical image classification accuracy by 1.7% using deformed attention and contributed to large-scale generative model data pipelines focused on quality and efficiency.
Microsoft
Jan 2022 - Jul 2022 (6 months)
Delivered FinOps and Dynamics 365 solutions and optimized Azure infrastructure for The Metropolitan Museum of Art, reducing cloud costs by 30% via resource allocation and automation.
Degrees, certifications, and relevant coursework
Master of Science, Computer Science
2022 - 2024
Grade: 8.0/10.0
Master’s in Computer Science completed with a GPA of 8.0/10.0, focusing on machine learning, LLMs, and deep learning applications.
Bachelor of Technology, Computer Science and Engineering
2018 - 2022
Grade: 8.54/10.0
Bachelor in Computer Science and Engineering completed with a GPA of 8.54/10.0, covering core software engineering and machine learning topics.
Software and tools used professionally
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