Hey! It's me Shachin,

Building Intelligent systems that learn, predict & transform.

I build end-to-end ML pipelines, real-time prediction systems, and AI-powered applications that transform complex data into scalable, real-world solutions.

Machine Learning
Deep Learning
AI Engineering
Neural Networks
MLOps
Computer Vision
NLP
Data Pipelines

About Me

I'm Shachin VP, an aspiring AI Research & Development Engineer with strong hands-on experience in Python, deep learning architectures, feature engineering, and statistical modeling. Experienced in building end-to-end ML pipelines, real-time prediction systems, and API-based deployments. Passionate about transforming complex data into scalable, real-world AI solutions through continuous learning and experimentation.

Featured Projects

Notable Projects

Selected Works

Delivering end-to-end data value, including: OrbitXOS Space Tracking, Senior Business Analyst Portfolio, Director of ABB Company Portfolio, JV Associate LLC Website, and Lead Gen & Email Automation.

Space Safety · AI & 3D

OrbitXOS Space Tracking

Freelance · Analytics

Senior Business Analyst Portfolio

Freelance · Corporate

Director of ABB Company Portfolio

Frontend · Web App

JV Associate LLC Website

Node.js · Automation

Lead Gen & Email Automation

My Approach

How I Approach a Project

Every project is different, but my process is consistent — from understanding the problem deeply to shipping robust systems that serve predictions reliably.

01
1. Scope & Objective Definition
Understanding the problem space, constraints, and success metrics
02
2. Data Pipeline Engineering
Ingestion, clean-up, feature extraction, and transformations
03
3. Architecture Design & Iteration
Selecting algorithms, model training, and hyperparameters
04
4. Rigorous Validation & Analysis
Evaluating performance, robustness, fairness, and bias
05
5. Production Deployment & Monitoring
Serving predictions at scale, tracking health, and retraining

1. Scope & Objective Definition

Understanding the problem space, constraints, and success metrics

Every successful AI model starts with a clearly defined problem. I work backwards from the business goal, translate it into technical ML objectives, define precise target metrics (F1-score, latency, etc.), and identify key data constraints.

Focus:Key Deliverables: Metric design, data sourcing plans, validation strategies

2. Data Pipeline Engineering

Ingestion, clean-up, feature extraction, and transformations

Great models are built on high-fidelity data. I build scalable ingestion pipelines, handle missing values and outliers systematically, run exhaustive statistical analyses for data drift, and perform advanced feature engineering to uncover predictive signals.

Focus:Tools: pandas, NumPy, scikit-learn, SQL, Feature Store integrations

3. Architecture Design & Iteration

Selecting algorithms, model training, and hyperparameters

I run structured experiments to identify the optimal model architecture. Starting from strong baselines, I iterate through state-of-the-art models (XGBoost, Transformers, LSTMs), perform hyperparameter tuning, and track experiments meticulously.

Focus:Frameworks: PyTorch, TensorFlow, MLflow for tracking & reproducibility

4. Rigorous Validation & Analysis

Evaluating performance, robustness, fairness, and bias

Accuracy isn't enough. I evaluate model robustness using cross-validation, examine confusion matrices, perform error analysis to uncover system weaknesses, test edge cases, and run bias audits to ensure equitable predictions across demographics.

Focus:Metrics: Precision-Recall curves, SHAP/LIME explainability, slice analysis

5. Production Deployment & Monitoring

Serving predictions at scale, tracking health, and retraining

I package models into optimized, high-throughput microservices. I implement API endpoints, containerize apps for deployment, set up real-time latency monitors, track feature drift in production, and build automated retraining loops.

Focus:Stack: FastAPI, Docker, Streamlit UI, live performance monitoring

Machine Learning

Deep Learning

AI Engineering

Neural Networks

MLOps

Computer Vision

NLP

Data Pipelines

Notable Projects

Selected Works

OrbitXOS Space Tracking

Senior Business Analyst Portfolio

Director of ABB Company Portfolio

JV Associate LLC Website

Lead Gen & Email Automation

How I Approach a Project

1. Scope & Objective Definition

2. Data Pipeline Engineering

3. Architecture Design & Iteration

4. Rigorous Validation & Analysis

5. Production Deployment & Monitoring

1. Scope & Objective Definition

2. Data Pipeline Engineering

3. Architecture Design & Iteration

4. Rigorous Validation & Analysis

5. Production Deployment & Monitoring