🎯 Senior DataScientist @ Microsoft | 📚 Published Author | 🔬 Published Researcher
"Bridging the gap between cutting-edge AI research and real-world applications through innovative cloud-native solutions"
am passionate about building the next generation of intelligent systems that transform industries and deliver real business impact. My core expertise lies in fine-tuning large, small, and mini language models, deep reinforcement learning for behavioral alignment of AI systems, and developing custom machine learning solutions purpose-built to meet specific business KPIs. At Microsoft Engineering, I tackle complex, high-stakes business problems using rigorous scientific and engineering approaches. Alongside my work, I actively contribute to the broader AI community through research, technical writing, and open-source projects.
- 🏗️ Build enterprise-scale AI systems as a Senior Data Scientist, turning complex data and ML models into production-grade applications.
- 📝 Share insights on AI, machine learning, and applied data science through technical writing on LinkedIn and Medium
- 🔬 Explore applied research in Deep Reinforcement Learning and Industrial AI, focusing on practical impact in real-world systems.
- 🌟 Mentor developers and engineers to design and build intelligent, scalable applications.
Peer-reviewed research contributing to the AI/ML and cloud systems community
Published in: Mathematical and Computational Applications (MCA), MDPI — Special Issue: 30th Anniversary of MCA
This paper presents a systematic taxonomy and evaluation of machine learning–driven autoscaling approaches for elastic cloud systems, spanning classical ML, deep learning, and reinforcement learning. By synthesizing insights from extensive prior research, it highlights key design patterns, evaluation metrics, and open challenges in building scalable and efficient cloud-native systems.
Co-authors: Srikanth Machiraju · Sahil Sharma, PhD, AFHEA · Vijay Kumar
| 📰 Full Paper | |
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| Read on MDPI → | Download PDF → |
Sharing knowledge through comprehensive guides on AI and cloud technologies
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📫 How to reach me: Write to [email protected] / ping me on linked-in
👯 I’m looking to collaborate on research work related to reinforcement learning.
⚡ Fun fact: I'm actually not as busy as it appears :)
sriksmachi/sriksmachi is a ✨ AI/ML ✨ repository where you can find all my work.
Here are some ideas to get you started:
- 🔭 I’m currently working on applied RL
- 🌱 I’m currently learning distributed ML systems
- 👯 I’m looking to collaborate on RL in the field of industrial automation
- 🤔 I’m looking for help with ...
- 💬 Ask me about ML/DS/AI, designing distributed systems for the cloud, microservices design
"Where Applied Research solves real-business problems"
The sriksmachi repository is a ✨ comprehensive AI/ML knowledge hub ✨ showcasing production-ready solutions, research implementations, and educational resources. Each section below contains battle-tested examples, interactive notebooks, and mini-projects applicable across industries.
Real-world applications showcasing AI innovation in action
| 🚀 Project | 🔗 Repository | 💡 Innovation |
|---|---|---|
| 🤖 Multi-Agent AI System | View Project → | Language acceleration for multi-agent systems |
| 🚕 SuperCabs | View Project → | RL/Q-Learning-based decision framework for car-rental services like uber, that maximises profit |
| 🏢 RBEI | View Project → | YOLO-based household object detection for edge devices & smart cleaning robots |
| 🔷 Azgentica | View Project → | Vision-powered AI agent transforming Azure architecture diagrams into structured insights & cost analysis |
Reinforcement Learning & Distributed ML Systems
- Exploring advanced techniques in RL applications for industrial automation [supply chain orders] and intelligent systems [RL-based decision system for AI trading with market sentiment analysis]
- Focusing on distributed training and large-scale model optimization
- Active experimentation with multi-agent systems and language model acceleration
Research Interests:
- 🤖 Deep Reinforcement Learning applications in robotics and automation
- 🔄 Distributed training for large-scale AI systems
- 🤝 Multi-agent AI systems and coordination
- ⚡ Language model optimization and acceleration techniques
- ☁️ Cloud-native distributed ML architectures
- 📈 ML/RL-based autoscaling for elastic cloud systems
How to Engage:
- 💬 Interested in collaborating on RL research? Reach out via LinkedIn
- 📝 Follow my research explorations on Medium
- 🔗 Explore my active research repositories above
The following links point you to AI ML topics that that can be learnt in 30 minutues with code and examples.
| 🔥 Domain | 🚀 Repository | 📊 Focus Area |
|---|---|---|
| 🌐 Azure ML | Explore → | Cloud-native ML solutions |
| 🧠 Large Language Models | Explore → | LLM applications & fine-tuning |
| 📈 Classical Machine Learning | Explore → | Traditional ML algorithms & Concepts |
| 🎮 Reinforcement Learning | Explore → | Reinforcement learning concepts & applications |
