Current Role
ML Generalist at Outlier AI — I refine LLM outputs and build high-quality annotation and evaluation workflows for model tuning.
Machine learning engineering that moves from experiments to dependable production systems.
I design full-stack ML pipelines across data engineering, modeling, and deployment. My work spans cybersecurity detection, NLP embeddings, cloud automation agents, and real-time computer vision — always with an MLOps-first approach.
Core Expertise
Self-supervised and contrastive learning, anomaly detection, model benchmarking, and RLHF-aligned review workflows.
Primary Stack
Python, PyTorch, TensorFlow, Scikit-learn, OpenCV, MLflow, Docker, AWS EC2, Firestore, Power BI.
Flagship Projects
Network Intrusion Detection, Lexical-Semantic Embeddings, Sales Forecasting, Cloud AI Automation Agent, Sign Language Interpreter.
MLOps Practice
I focus on reproducible experimentation, model versioning, CI/CD, deployment lifecycle management, and low-latency inference optimization.
Education
Bachelor of Engineering in Computer Science at SRM Institute of Science and Technology with advanced ML coursework.
Certifications
Oracle Cloud Infrastructure Foundations Associate plus competitive GenAI hackathon experience and finalist recognition.
Languages
English and Hindi with clear communication across technical and cross-functional teams.
2025-Present
Outlier AI ML Generalist — I improved instruction-following quality and reduced hallucination risk through structured adversarial evaluations.
2025
Developed and deployed cloud AI automation workflows on AWS EC2 with n8n, API integrations, and persistent state management.
2024-2025
Delivered multi-domain ML projects in cybersecurity, NLP, forecasting, and vision — always with production-focused evaluation methods.