ML / AI Systems Candidate

Building a strong foundation in applied machine learning, operational analytics, automation, and support-focused AI systems.

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Rakesh Ravikumar

• Junior Machine Learning / AI Systems Candidate
Open to junior ML / AI / analytics opportunities

Background

I come from a technical support and IT operations background, where structured troubleshooting, escalation quality, and repeatable operational workflows became core strengths. I am now applying that same systems mindset to machine learning, automation, and support-oriented AI projects.

Structured Troubleshooting Operational Analytics Applied Machine Learning
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5+

Years in Technical Support & IT Operations

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3

Featured Applied AI / ML Projects

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20+

Network & Service Incidents Resolved Daily

2024–26

MSc in Artificial Intelligence

🏅 Certifications & Courses

Continuous learning and professional development across multiple domains

Udemy_Logo
✓ Verified

Python for Data Science and Machine Learning Bootcamp

Udemy • 2018

Tensorflow_Logo
✓ Verified

TensorFlow / Deep Learning Fundamentals

Udemy • 2025

Tensorflow_Logo
✓ Verified

Google Cloud Machine Learning Engineer Path

Google • In Progress

Technical Stack

Technologies and tools I use to build innovative solutions

Pandas

30 projects
Proficiency 92%

Data manipulation and analysis

Scikit-learn

25 projects
Proficiency 90%

Machine learning and predictive modeling

NumPy

25 projects
Proficiency 90%

Numerical operations and array processing

TensorFlow

15 projects
Proficiency 85%

Deep learning and neural networks

NLP

20 projects
Proficiency 88%

Natural language processing & classification

AI & ML Expertise

Specialized in cutting-edge AI technologies and modern machine learning architectures

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Large Language Models

Advanced fine-tuning and deployment of state-of-the-art language models

Technologies

GPT-4 Claude Llama 2/3

Key Achievements

  • Fine-tuned 15+ custom LLMs for domain-specific tasks
  • Implemented LoRA/QLoRA for efficient parameter updates
  • Deployed production-ready inference endpoints
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Transformer Architecture

Deep understanding of attention mechanisms and neural network architectures

Technologies

BERT T5 Vision Transformers

Key Achievements

  • Built custom transformer models from scratch
  • Optimized attention mechanisms for efficiency

Generative AI & GANs

Creating synthetic data and generative models for various applications

Technologies

StyleGAN Diffusion Models ControlNet

Key Achievements

  • Developed custom GAN architectures for image synthesis
  • Implemented diffusion models for high-quality generation