I build AI systems and infrastructure that scale — from fine-tuning models rooted in mathematical understanding, to production-grade distributed systems.
"My standard as an engineer is to be the highest-value hire in any technical evaluation — not by chasing frameworks, but by going deeper than most are willing to: into the mathematics, the infrastructure, and the edge cases that separate good from irreplaceable."
// Real production systems, not toy demos
Production RAG pipelines, fine-tuned LLMs (LoRA/QLoRA), multi-turn agents with persistent memory. Optimized for token cost and p95 latency.
↳ Voice agent with <800ms V2V latency
DAG-based agent orchestration where specialized AI agents collaborate, debate, and route with their own memory, tools, and decision boundaries.
↳ 5-agent Competitor Intelligence platform
Linear/Logistic Regression, SVMs, Gradient Boosting (XGBoost/LightGBM), Ensemble methods, Bayesian models, k-NN, Decision Trees, and Random Forests. I understand the mathematics underneath, not just the API calls.
↳ Feature engineering, cross-validation, hyperparameter tuning
CNNs, RNNs, LSTMs, Transformers from scratch. Backpropagation, weight initialization, batch normalization, regularization — built and trained, not just loaded from a checkpoint.
↳ Fine-tuned DistilBERT on SST-2 with self-healing DAG
Object detection, image classification, semantic segmentation, and deepfake/proxy detection. Built CV pipelines for real-world production, not just Kaggle.
↳ Deepfake detection with <2% false positives in production
I own the full delivery lifecycle: IaC, containerization, CI/CD, monitoring, and rollback. Every model I ship has metrics, alerts, and a path back.
↳ Multi-node AWS deployment with automated CI/CD
Problem → Production, every time.
STEP 01
Scope · Success Criteria · Constraints · Stakeholders
Before any model is chosen, I reverse-engineer from the business outcome. What does success look like numerically? What are the latency, cost, and accuracy constraints? Who are the stakeholders and what decisions will this system power? I write a one-page problem brief that everyone can sign off on — if that's unclear, no model will fix it.
Production systems, not toy demos
Distributed AI platform using LangGraph, Gemini 1.5, and RAG to autonomously monitor competitors. Processes ~30K tokens/run extracting pricing shifts, hiring trends, and generating sales battlecards.
Production-ready ML pipeline with Terraform, Ansible, Docker, and FastAPI on multi-node AWS with Nagios monitoring and automated CI/CD via GitHub Actions.
// SKILLS UNIVERSE
Drag to rotate · Hover any tech to highlight · Auto-rotates when idle
Where I've built production AI systems
Banao Technologies
BlockseBlock
Looking for roles at the intersection of AI engineering and systems architecture. Open to complex distributed AI challenges.
Ayush Kumar · 2026 · Phagwara, India