Building production-grade ML systems & LLM pipelines that scale, explain themselves, and keep getting better.
Senior AI Engineer with 3+ years of experience building production-grade Python backend systems and end-to-end ML pipelines in enterprise environments. I thrive at the intersection of GenAI innovation and robust system engineering.
Experienced in Flask-based API engineering, LLM-powered log intelligence, MLOps automation, model deployment, and observability platforms. Proven track record delivering scalable ML solutions for anomaly detection, failure prediction, and automated remediation using state-of-the-art GenAI and deep learning frameworks.
Strong ML/DL/GenAI foundation from IIT Roorkee, with deep hands-on expertise across the full ML lifecycle — from data engineering and model training to deployment, monitoring, and CI/CD automation. Passionate about systems that don't just work — they scale, explain themselves, and keep getting better.
From raw data ingestion to production inference — I design and own the full ML system lifecycle. Below are the key architectural patterns and pipelines I've built and shipped at enterprise scale.
Production LLM pipeline combining FAISS vector search, OpenAI/HuggingFace embeddings, and prompt-chaining for automated root cause analysis on enterprise log data. Reduced mean debug time by 60% and eliminated alert fatigue across teams.
Complete ML system from data ingestion (Kafka, NiFi) → feature engineering → model training → MLflow tracking → BentoML serving → Flask API → monitoring dashboards. Live on thousands of infrastructure nodes.
End-to-end X-ray analysis system: data preprocessing → EfficientNet/ResNet with custom CNN layers → Grad-CAM visualization for clinical interpretability → production inference API.
Automated CI/CD architecture using Jenkins + n8n + Python covering: environment provisioning, cross-OS test execution, AI-generated unit test suites via GitHub Copilot, versioning, artifact publishing, and deployment gates. Cut release cycles by 40–50%.
I write about AI, machine learning systems, and the craft of building production-grade software. Published on Substack — thoughts on GenAI, MLOps, and the future of intelligent systems.
Thoughts on designing intelligent systems that go beyond the hype — practical insights into LLM pipelines, RAG architectures, and what it actually takes to ship GenAI in production.
Read on Substack →Exploring the full ML lifecycle: from telemetry ingestion and anomaly detection to production deployment. Real lessons from building observability platforms at enterprise scale.
Read on Substack →End-to-end X-ray COVID-19 detection pipeline using EfficientNet/ResNet with custom CNN layers. Applied Grad-CAM to visually highlight infected lung regions, improving model interpretability for clinical validation workflows.
Complete price prediction ML pipeline with advanced feature engineering across brand, CPU, RAM, and GPU attributes. Achieved 15% accuracy improvement through systematic hyperparameter tuning using XGBoost and Random Forest optimization.
Rapid migration of a production data ingestion platform from GCP Pub/Sub to Kafka & Apache NiFi using AI-assisted GitHub Copilot prompts. Significantly reduced cloud infrastructure costs and achieved full production deployment within days.
Implemented AI-powered API unit testing pipelines that automated test generation and execution using LLM prompt engineering and GitHub Copilot. Reduced manual testing effort substantially, accelerated release cycles, and minimized production defects.
Open to exciting new opportunities in ML Engineering, GenAI Systems, and Backend roles. Whether you have a project in mind, want to collaborate, or just want to talk AI — I'm all ears. Let's build something incredible together.