+ Building reliable, production-grade AI systems across Generative AI,
+ multivariate time-series modeling, and ML infrastructure|
🧠 LLM Workflows - RAG, Agents, Fine-tuning |
class MLEngineer:
def __init__(self):
self.focus = "Production systems"
self.priority = "Reliability > Accuracy"
self.approach = "Understand failures first"
def build(self):
return "Stable AI systems at scale" |
Aditya Birla Management Corporation (GDNA) • Bengaluru
Sep 2025 - Dec 2025
- 🏭 Manufacturing analytics for Hindalco
- 📈 Predicted Particle Size Distribution (PSD)
- 🔬 Built synthetic multivariate time-series with diffusion models
- 🎯 Temporal & feature self-attention mechanisms
- ✅ Validated using DTW, PCA, t-SNE
TeamLease RegTech Pvt. Ltd. • Pune
Dec 2024 - Jun 2025
- 📄 Built Flask REST APIs for document intelligence
- 💬 Developed RAG-based chatbots with vector databases
- ☁️ Integrated Azure Cognitive Services & AI Search
- 🔧 LLM fine-tuning (LoRA/QLoRA) for compliance
- 🐳 Docker + Azure containerized deployments
|
Multi-Agent SQL Generation System
|
Enterprise Document Intelligence
|
|
Computer Vision Authentication
|
Automated Certificate Validation
|
|
Diffusion Models |
Production RAG Pipelines |
Advanced Transformers |
| 🎓 | Education: Engineering Background | 🌍 | Location: India |
| 💡 | Interests: AI Safety, Model Interpretability | 🎯 | Goal: Reliable AI Systems at Scale |
| 🔧 | Approach: Understand Failures First | 📊 | Focus: Production over Prototypes |
const opportunities = [
"Generative AI Engineer",
"ML Engineering",
"Applied AI Research",
"LLM Systems Development"
];