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Hi, I'm Surendranath Duvvuru 👋

AI/ ML Engineer | Generative AI & LLM Research | Aspiring AI Architect

Building practical AI solutions by combining Machine Learning, Deep Learning, LLM-based reasoning, and a strong Engineering mindset.

📍 Based in The UAE · Open to opportunities in AI/ ML Engineering, Generative AI, and AI Architecture

GitHub LinkedIn


  • 🎓 Master of Science in Artificial Intelligence (AI) and Machine Learning (ML).
  • 📜 Certified in Machine Learning (ML) and Artificial Intelligence (AI).
  • 🤖 I bring 2+ years of ML engineering experience alongside 14+ years in Software testing and Performance engineering.
  • 🧠 Interested in Gen AI, Large Language Models (LLMs), AI Agents, Tool-augmented reasoning, Production-ready ML systems.
  • 🔬 Academic research on: Improving Tool-use efficiency in LLM-based AI Agents, including Chain-of-Abstraction (CoA) reasoning.
  • 🏗️ Working on to design reliable, scalable AI systems with an focus on eval, performance, maintainability, responsible deployment.
  • 🌍 Based in the United Arab Emirates (UAE) and interested in opportunities where AI can solve meaningful business problems.
  • 🚀 I bring together an AI/ ML learning and building mindset with years of experience in quality, performance, and systems thinking.

🛠️ Technical Skills

AI, Machine Learning & Research

  • Machine Learning · Deep Learning · Model Evaluation
  • Generative AI · Large Language Models (LLMs) · Prompt Engineering
  • AI Agents · Tool-Augmented Reasoning · Chain-of-Thought (CoT) / Chain-of-Abstraction (CoA) concepts
  • Natural Language Processing (NLP) · Computer Vision

Languages & Data

  • Python · SQL
  • Pandas · NumPy · SciPy · scikit-learn
  • Matplotlib · Seaborn · Jupyter Notebook

Deep Learning & MLOps

  • PyTorch · TensorFlow
  • Hugging Face Transformers
  • MLflow · Git · Docker
  • AWS and Microsoft Azure (currently developing cloud skills)

Engineering Foundations

  • Performance Testing · Software Quality Engineering
  • Test Planning · Troubleshooting · Root-Cause Analysis
  • Performance Analysis · Reliability and scalability thinking

🔬 Research Interests

My academic interests include making LLM-powered systems more effective at using tools and solving multi-step tasks.

  • LLM Agents: planning, reasoning, and tool selection.
  • Chain-of-Abstraction (CoA) reasoning: separating high-level reasoning from tool execution.
  • Evaluation: task accuracy, latency, and tool-call efficiency.
  • Experimentation: comparing reasoning and tool-use approaches on question-answering and mathematical reasoning tasks.

👨🏻‍🎓 Education and Certifications

  • M.Sc in AI and ML (On-line) — Liverpool John Moores University (LJMU), Liverpool, UK
  • Executive Post Graduate Program (ePGP) in ML and AI (On-line) — International Institute of Information Technology-Bangalore (IIIT-B), India
  • B.Tech in Computer Science — Jawaharlal Nehru Technological University (JNTU), Hyderabad, India

💼 Professional Background

  • Machine Learning Engineering: 2+ years of experience, supported by postgraduate study and project work in AI/ ML.
  • Software Testing & Performance Engineering: 14+ years of experience in enterprise environments, including performance testing, analysis, and troubleshooting.
  • Mainframe Development: earlier experience in enterprise application development.

This background helps me approach AI systems with attention to measurable performance, robustness, testing, and operational quality.

🤝 Let's Connect

I'm interested in connecting with AI/ ML practitioners, Researchers, Hiring teams, and Technology leaders across the UAE.


Learn continuously. Build thoughtfully. Measure what matters. Create AI that helps.

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  1. Optimizing_The_Tool_Selection_and_Usage_Ability_Process_of_LLM_Based_AI_Agents Optimizing_The_Tool_Selection_and_Usage_Ability_Process_of_LLM_Based_AI_Agents Public

    A Chain-of-Abstraction (CoA) framework to optimize tool selection and usage in LLM-based AI agents by separating abstract reasoning from execution, improving efficiency, accuracy, and reliability

    Python

  2. Sentiment-Aware_Product_Recommendation_System_using_NLP_and_Collaborative_Filtering Sentiment-Aware_Product_Recommendation_System_using_NLP_and_Collaborative_Filtering Public

    A Product Recommendation System that combines NLP, machine learning-based sentiment classification, and collaborative filtering to deliver personalized product recommendations. Implemented text pre…

    Jupyter Notebook

  3. Intelligent_Lead_Scoring_and_Conversion_Prediction_using_Machine_Learning_and_MLOps Intelligent_Lead_Scoring_and_Conversion_Prediction_using_Machine_Learning_and_MLOps Public

    An intelligent lead-scoring system using Python, PyCaret, and LightGBM to predict application completion based on acquisition channels and customer engagement patterns. Designed an MLOps-oriented w…

    Jupyter Notebook

  4. Identifying_Entities_in_Healthcare_Data_using_NER Identifying_Entities_in_Healthcare_Data_using_NER Public

    Syntactic processing of Healthcare Data to identify entities using Named Entity Recognition (NER), spaCy for part-of-speech (POS) tagging and Conditional Random Fields (CRF) for token-level sequenc…

    Jupyter Notebook

  5. Hand_Gesture_Recognition_using_Deep_Learning Hand_Gesture_Recognition_using_Deep_Learning Public

    Hand Gesture Recognition using Conv3D & ConvGRU approach in Deep Learning Neural Networks

    Jupyter Notebook

  6. Melanoma_Cancer_Detection_using_CNN Melanoma_Cancer_Detection_using_CNN Public

    Melanoma Cancer Detection solution using Convolutional Neural Networks

    Jupyter Notebook