Hi there, I'm Gokul Krishna 👋

Machine Learning Engineer | MLOps Specialist | AI Workflow Architect

🚀 As a passionate ML engineer, I specialize in building scalable, production-grade AI solutions with a focus on automation, reliability, and performance.
I thrive at the intersection of data science and DevOps, where engineering meets intelligence.

🧠 With hands-on experience in predictive modeling, real-time inference APIs, and automated training pipelines,
I enjoy solving real-world problems using tools like FastAPI, MLflow, DVC, Celery, and AWS.

🎯 Whether it’s deploying intelligent systems, optimizing simulation-based models, or orchestrating end-to-end pipelines —
I aim to build AI that not only works, but works smart.

📌 Explore some of my featured projects below to see my work in action.

🚀 Featured Projects

📑 Blog Generation with AWS Lambda + Bedrock + S3

A serverless, AI-powered blog generation system featuring:

Amazon Bedrock foundation models for AI text generation
AWS Lambda for serverless orchestration
Amazon S3 for persistent blog storage
API Gateway for secure, HTTPS-based client access
Fine-grained IAM roles for secure Bedrock and S3 integration

🔗 Tech: AWS Lambda, Amazon Bedrock, API Gateway, Amazon S3, Python
📂 Design: Serverless, cloud-native, scalable, production-ready

📝 Text Summarizer — Google Pegasus LLM Fine-Tuning Pipeline

A production-grade, end-to-end abstractive text summarization system featuring:

Full fine-tuning of Google Pegasus LLM for domain-specific summarization.
Modular, MLOps-ready pipelines for ingestion, validation, transformation, training, evaluation, and prediction.
FastAPI backend with Jinja2-powered web UI.
DVC dataset versioning, MLflow experiment tracking, and centralized logging.
Local + AWS S3 artifact storage with YAML-driven configuration.

🔗 Tech: FastAPI, Hugging Face Transformers, Google Pegasus, DVC, MLflow, AWS S3, YAML
📂 Design: Modular, reproducible, cloud-ready, production-grade

🔐 Phishing Detection API with MLOps

A fully modular, production-grade ML pipeline for phishing detection with:

FastAPI for API serving (/train, /predict)
Async training using Celery + Redis
Workflow versioning with DVC
Hyperparameter tuning via Optuna
Experiment tracking using MLflow + DagsHub
Schema + drift validation, preprocessing, and artifact management
CI/CD with GitHub Actions, auto-sync to AWS S3

🔗 Tech: FastAPI, Scikit-learn, Celery, MLflow, Optuna, AWS, MongoDB, Docker
📂 Design: Modular, YAML-driven, CI/CD-enabled

📊 Student Performance Prediction – ML Pipeline

A reusable and scalable machine learning pipeline to predict student scores:

PostgreSQL-based ingestion with table auto-creation from YAML schema
Configurable preprocessing (scaling, encoding, imputing, column operations)
Full pipeline: ingestion ➜ validation ➜ transformation ➜ training ➜ evaluation ➜ prediction
Optuna-powered hyperparameter tuning with MLflow tracking
Centralized logging with AWS S3 and local backups
Artifact versioning using DVC

🔗 Tech: Scikit-learn, Optuna, PostgreSQL, MLflow, DVC, AWS, YAML
📂 Design: Production-grade, modular, reusable, data-driven, CI/CD-enabled

🍷 Wine Quality Prediction – Regression Pipeline

A Dockerized regression pipeline with a simple web interface for real-time prediction:

Built using ElasticNet Regression
Flask frontend for user input and result display
Modular pipeline: ingestion → validation → transformation → training → prediction
Structured with reusable entity-based config classes
MLflow-based experiment logging & reproducible config

🔗 Tech: Flask, Scikit-learn, MLflow, Docker, YAML
📂 Design: Lightweight, extensible, production-ready

💼 Professional Experience

🔹 Data Scientist / CAE Engineer – General Motors (via TCS)
Sep 2019 – Aug 2023 · Bangalore, India

Reduced EV battery module mass by 11% via ML-driven simulation optimization
Automated FEA workflows and built predictive models for unseen load conditions
Developed Kriging-based optimization and mentored junior engineers in ML-integrated engineering

🔹 Structural Analyst – Johnson & Johnson MedTech (via TCS)
Feb 2017 – Aug 2019 · Kolkata, India

Optimized design cycles for surgical devices using FE analysis and statistical tools
Built automation scripts for structural simulation and data extraction
Supported design for novel uterine tumor excision device using biomechanical modeling

🧩 About Me

🛠 I’m currently working on
Production-grade AI APIs with FastAPI + Celery + MLflow for scalable ML deployment.
🤝 I’m looking to collaborate on
Applied ML projects, MLOps tooling, or LLM use cases in healthcare or finance.
🧠 I’m looking for help with
Model serving at scale and efficient Kubernetes-based deployment.
🌱 I’m currently learning
LLM fine-tuning, Kubeflow pipelines, and advanced ML monitoring strategies.
💬 Ask me about
MLOps pipelines, Optimization, Automation, or real-world AI deployment.
⚡ Fun fact
My first ML pipeline was trained entirely on FEA simulation data—no labeled dataset, just raw physics!