Follow me for temporal intelligence insights, AI/ML engineering, and rigorous analysis that cuts through the hype.
Building AI systems that actually work, backed by math that actually matters.
AI Software Engineer combining research rigor, mathematical depth, and innovative tool building to advance AI engineering through novel approaches and comprehensive developer workflows.
What drives me most in AI/ML is the R&D and innovation side. Taking cutting-edge research and turning it into real-world solutions. There's something exciting about being at that intersection of theory and practical impact.
⇒ Research Rigor - Statistical validation, mathematical analysis, and reproducible methodologies
⇒ Mathematical Foundations - Deep expertise in core ML mathematics (SVD, linear algebra, statistical modeling)
⇒ Innovation Leadership - Temporal Intelligence and novel approaches to AI system analysis
⇒ Developer Tools - Building comprehensive workflows that solve real engineering problems at scale
⇒ R&D & Innovation - 10+ years across fintech, ad-tech, and enterprise SaaS | MS AI/ML, MS CS (in-progress)
I build AI systems that bridge mathematical rigor with practical engineering. My work spans temporal pattern analysis, rigorous AI agent benchmarking, foundational ML mathematics, and creating tools that solve real problems developers face daily.
Specializing in research-driven innovation, mathematical ML foundations, comprehensive developer tooling, and statistical validation. I build in public, treat open source like product, and advance the field through rigorous analysis and thoughtful engineering.
▶ Mission: Advancing AI engineering through research rigor, mathematical depth, and innovative tools that empower both humans and machines.
Temporal Intelligence - Analyzing AI systems and code evolution patterns over time
AI Agent Architecture - Rigorous benchmarking and mathematical validation of agent systems
Neural Network Training & Optimization - Advanced fine-tuning methodologies, QLoRA implementations, and efficient training pipelines
Model Architecture Research - Agent-based learning systems and multi-model coordination frameworks
Mathematical ML Foundations - Bridging core mathematics with modern AI applications
Next-Gen AI Tools - Building developer workflows for the future of AI engineering
Mathematical benchmark exposing the massive performance gap between real agents and LLM wrappers.
◊ Rigorous multi-dimensional evaluation ◊ Statistical validation (95% CI, Cohen's h) ◊ Separating theater from real systems
Features stress testing, network resilience, ensemble coordination, and failure analysis with reproducible methodology.
Temporal Code Intelligence platform predicting quality evolution through Git history analysis.
◊ Conversational AI agent ◊ Code complexity trends ◊ Mathematical pattern analysis
Revolutionary approach to understanding codebase evolution with decay forecasting and maintenance burden prediction.
Experimental framework for multi-agent coordination and collaborative learning architectures.
◊ Agent-based learning systems ◊ Coordination protocols ◊ Emergent behavior analysis
Research platform exploring advanced agent interactions, training methodologies, and collective intelligence patterns.
Clean UI for LLM development workflows with prompt versioning and model selection.
◊ Built for engineers, not hype ◊ Streamlined workflow ◊ Multi-provider support
Prompt → model → tag → export workflow. Currently supports OpenAI, Claude, and Ollama.
Advanced 4-bit QLoRA fine-tuning pipeline for LLaMA 3 8B with production-grade optimization.
◊ Memory-efficient training ◊ Consumer GPU optimization ◊ Instruction-following specialization
Demonstrates cutting-edge parameter-efficient fine-tuning with Unsloth integration. Live demo at HF Space.
 GoC Mitra
High-performance AI-powered Git commit assistant with pluggable architecture. |
 LLMOps Dashboard
Modular framework for composing and debugging complex prompt pipelines. |
Research Rigor ~ No hand-waving. Statistical validation, reproducible methods, mathematical proof
Mathematical Foundations ~ Deep ML mathematics applied to real systems (not just theory)
Advanced Training Methods ~ Parameter-efficient fine-tuning, QLoRA optimization, multi-agent coordination
Neural Architecture Research ~ Agent-based systems, collaborative learning, emergent behaviors
Innovation ~ Temporal Intelligence and approaches that advance the field
Practical Engineering ~ Tools solving problems developers actually face
Open Innovation ~ Building in public with complete transparency and rigorous validation
In a field full of hype, I build systems backed by math.
Research rigor • Mathematical depth • Innovation • Next-generation AI/ML engineering
⋆ Star my repos if this work resonates with you