Hi, I'm Rakesh|
Junior AI/ML Builder | Applied ML • GenAI/RAG • AI Systems / MLOps
Junior AI/ML builder with 5+ years in technical support and IT operations, focused on three practical capability lanes: Applied Machine Learning, Generative AI / RAG applications, and AI Systems / MLOps foundations.
Structured Troubleshooting
Strong foundation in fault isolation, repeatable troubleshooting workflows, and structured technical problem-solving built through real production support environments.
Core Technical Stack
Technologies aligned with my resume, coursework, and applied machine learning projects.
Python
SQL
Pandas
NumPy
Scikit-learn
Docker
Linux
TensorFlowApplied Machine Learning
Building practical machine learning projects in prediction, classification, model evaluation, and operational analytics with a focus on real-world business use cases.
Generative AI / RAG Applications
Designing practical GenAI and retrieval-augmented applications that combine LLMs, embeddings, vector search, and structured workflows for grounded, task-focused assistance.
AI Systems / MLOps Foundations
Building deployment-aware AI systems with API-first design, Docker-based environments, orchestration patterns, and practical MLOps foundations for reliable real-world delivery.
Code Showcase
Selected snippets inspired by my machine learning, automation, and support-focused AI projects.
Telecom Churn Prediction
Feature prep, imbalance handling, and model training...
PCAP StoryTeller
Packet parsing and readable troubleshooting narratives...
Support Copilot
Memory, retrieval, and tool-assisted support workflow...
Telecom Churn Prediction - Model Training Flow
// Advanced RAG Pipeline with Multi-Agent Orchestration
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import classification_report
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
model = RandomForestClassifier(
n_estimators=300,
max_depth=12,
random_state=42
)
model.fit(X_train, y_train)
preds = model.predict(X_test)
print(classification_report(y_test, preds))
How I Build AI / ML Projects
Problem Framing
I start by defining the operational or business problem clearly, identifying the target outcome, constraints, and what "useful" means for the end user.
Data & System Design
I structure the input data, design the processing workflow, and choose the right approach—classical ML, analytics, or LLM-based systems—based on the problem and delivery requirements.
Build, Evaluate, Improve
I prototype quickly, evaluate outputs for reliability and usability, then refine the system for stronger performance, clearer results, and practical deployment readiness.