Hi, I'm Amey 👋
I build & deploy AI agents and LLM systems.
AI Engineer with an M.S. in Artificial Intelligence from University at Buffalo and production experience shipping LLM-powered systems — from AI agents backed by custom MCP tool servers to multimodal RAG pipelines and scalable Python backends on Azure and AWS.
About Me
Hello! I'm Amey Managute, an AI Engineer with an M.S. in Artificial Intelligence from the University at Buffalo. I ship production LLM-powered systems — AI agents backed by custom MCP tool servers, multimodal RAG pipelines, and scalable Python backends.
I've built AI agents in Microsoft Copilot Studio with custom OAuth-secured MCP servers, multimodal image-generation engines on Google Gemini, and dual-index RAG pipelines with Pinecone and FastAPI. My work spans Azure and AWS, from stateless, horizontally scalable backends to containerized ML services.
I work most with Python, PyTorch, FastAPI, LangChain, and the major LLM APIs. Whether I'm architecting a retrieval pipeline or deploying a scalable service, I focus on building things that are fast, reliable, and maintainable.
Experience
AI Engineer
ProMazo (Client: Dynatrace)
- Built an AI agent in Microsoft Copilot Studio that automates document workflows across company research, Excel model computation (OpenPyXL), and PPTX rendering, powered by a custom OAuth 2.0-secured MCP tool server architected in Python, cutting analyst turnaround from hours to minutes
- Engineered a stateless, horizontally scalable backend using Azure Redis for session state and Azure Files for artifact staging across replicas, with CI/CD via Azure DevOps and Azure Container Jobs automating TTL-based artifact cleanup to control production storage costs
AI Engineer Intern
FutureHouse.ai
- Built a multimodal AI image-generation engine on Google Gemini Flash Image supporting up to 5 reference images with face identity preservation, background removal, and platform-aware output, resolving a production Postgres timeout by offloading assets to Supabase Storage
- Engineered 4 LLM generation pipelines on Google Gemini behind a shared orchestration layer with multi-stage JSON repair
Machine Learning Intern
The Tann Mann Gaadi
- Built a natural-language query interface for 50+ non-technical users, cutting average query time by 40%, and automated ETL and feature-engineering pipelines with LangChain and PandasAI, reducing data processing time by 60%
Education
Master of Science – Artificial Intelligence
University at Buffalo, The State University of New York
- GPA: 3.33/4.0
- Specialization in Artificial Intelligence and Machine Learning
- Coursework: Applied Machine Learning, Deep Learning, Natural Language Processing, Computer Vision
Bachelor of Engineering – Artificial Intelligence and Data Science
University of Mumbai
- CGPA: 8.44/10
- Focus on Computer Science fundamentals, Data Structures, Algorithms, and Software Engineering
- Developed a strong foundation in programming and system design
Projects
Manim MCP Server
A Model Context Protocol server that lets LLM clients generate 3Blue1Brown-style math animations from natural language. A 5-tool API cuts workflows from 30-50 granular operations down to 2-3 calls, with segment-based composition and a Pydantic-validated video rendering pipeline.
GPT from Scratch
Character-level generative language model built from scratch using GPT architecture. Includes tokenization, multi-head self-attention, positional encodings, and temperature-based text generation.
Image Forgery Detection
ELA + CNN pipeline to detect and highlight manipulated regions in images. Built with React frontend, Flask backend, and TensorFlow.
Skills
Core Languages
ML & AI Frameworks
GenAI & LLMs
Backend & APIs
MLOps & DevOps
Cloud (AWS & Azure)
Get In Touch
Let's Connect
I'm always open to conversations about AI, ML, and software development. Whether you have a question, want to collaborate, or just want to say hi, feel free to reach out!