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AI / ML Engineer · Production Systems

ChitranshSaxena

Resume

I build real production AI/agentic systems, not toy projects

ARCHITECTURE & ETHOS

ENGINEERINGFORproductionreality.

I bridge the gap between experimental LLM research and software that survives contact with real users. Graduated 2026, already shipping production AI systems that operate at scale.

Most AI projects fail at the boundary of demo and deployment. My work centers on building autonomous agent architectures, multi-tenant conversational platforms, and advanced RAG pipelines that withstand real-world enterprise constraints.

I graduated in 2026 with a B.Tech in Computer Science from Manipal University Jaipur, having already led production AI projects for industrial clients across Canada, Europe, Australia, and beyond.

Outside of production systems, I touch grass through badminton and questionable gym form.

I specialize in

Retrieval systems, language models, and going outside occasionally. Designing and building reliable AI systems, from intelligent retrieval and language models to production-ready backend infrastructure and end-to-end applications.

(01)

AI Engineering & Applied Machine Learning

Building intelligent systems across LLMs, retrieval, deep learning, computer vision, and domain-specific AI, with a focus on turning models into useful, reliable applications.

  • 01LLMs, RAG & semantic retrieval
  • 02Deep learning, computer vision & NLP
  • 03Model fine-tuning, evaluation & MLOps
(02)

Fullstack Development

Building end-to-end applications that connect polished interfaces with reliable APIs, data layers, authentication, and real-world integrations.

  • 01React, Next.js & modern frontend architecture
  • 02REST APIs, databases & authentication
  • 03Cloud integrations, deployment & production workflows
(03)

Backend & Systems Engineering

Engineering the infrastructure behind reliable software, from microservices and APIs to distributed systems, observability, and scalable data services.

  • 01FastAPI, Flask, Node.js & Go
  • 02Microservices, Redis, vector databases & multi-tenancy
  • 03Docker, CI/CD, observability & cloud infrastructure
(00) — AI / Multi-Agent Systems / Intelligent Automation

AI-Assisted Scraper

ReactExpressFastAPIChromaDBReinforcement LearningDockerPlaywright
AI-Assisted Scraper
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A multi-service AI content pipeline that scrapes web pages, extracts content and screenshots, rewrites material with LLMs, supports contextual review, maintains version history through ChromaDB, and uses a reinforcement-learning service for review feedback. The system is containerized with Docker Compose, includes service health checks, automated tests, SSRF protection, CORS origin controls, and persistent vector storage.

(05) — Backend Engineering / Distributed Systems / Observability

Centralized API Orchestration Engine

GoAPI GatewayRedisOpenTelemetryPrometheus & GrafanaChaos EngineeringDocker
Centralized API Orchestration Engine
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A Go-based API gateway designed around a consistent request-processing pipeline covering logging, metrics, tracing, tenant resolution, analytics, rate limiting, chaos injection, and reverse proxying. Redis-backed token-bucket rate limits persist across restarts, while Prometheus, Grafana, and OpenTelemetry provide end-to-end observability. The project includes a live gateway, public Grafana dashboard, metrics endpoints, and controlled latency, failure, and packet-drop experiments.

(11) — LLM Engineering / Legal AI / Model Deployment

Legal Llama 3.1 Part 5

Llama 3.1TRL & UnslothvLLMSGLangGGUF & llama.cppTransformersPyTorch
Legal Llama 3.1 Part 5
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A published Llama 3.1-based language model artifact focused on legal-domain text generation. The model is distributed in multiple inference-friendly formats and can be served through Transformers, llama.cpp, vLLM, SGLang, Ollama, and Unsloth, including OpenAI-compatible inference endpoints. The repository demonstrates hands-on model packaging and deployment across modern LLM inference stacks.

(01) — Machine Learning / Computer Vision / Medical AI

CKD-GAN Augmentation

PyTorchACGANSMOTETransfer LearningComputer VisionEfficientNetV2
CKD-GAN Augmentation
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A hybrid medical-image augmentation pipeline for chronic kidney disease classification using CT kidney imagery. It combines SMOTE for feature-space balancing with an Auxiliary Classifier GAN for synthetic image generation, then benchmarks VGG16, EfficientNetV2, and MobileNetV2 across original and augmented datasets. Reported accuracies range from 95.1% to 99.2%, with training runs documented for reproducibility.

(02) — Browser Engineering / Privacy / Security

SafeExtensions

Chrome Extensions APIManifest V3IndexedDBLocal Risk Analysis
SafeExtensions
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A privacy-first Chrome extension that locally audits installed extensions for potentially risky permissions, broad host access, and known tracker domains. It provides deterministic 0-10 risk scoring, severity indicators, CSV reporting, local persistence through IndexedDB, and direct disable/uninstall actions. The architecture performs analysis entirely on-device with no telemetry or remote calls.

Portfolio / Selected Dev Works

Projects

Backend & Infrastructure
  • FastAPI
  • Docker
  • Kubernetes
  • Redis
  • Qdrant
  • PostgreSQL
  • AWS
  • REST APIs
  • CI/CD
  • Nginx
AI/ML & Agentic Systems
  • LangGraph
  • RAG
  • Multi-Agent Systems
  • Prompt Engineering
  • PyTorch
  • TensorFlow
  • Hugging Face
  • NLP
  • Computer Vision
  • Scikit-learn
Core Engineering
  • Python
  • C++
  • SQL
  • DSA (200+ LeetCode)
  • System Design
  • Git
  • Linux
  • MongoDB
  • Statistical Analysis
  • Bash

Tech Stack I'vemastered

Timeline

To be continued...

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[ Version Control ]
0+Commits Pushed

ACTIVE REPOSITORIES

[ Hosts ]
0+Events Hosted
[ Builds ]
0+Hackathons Participated
[ Collaborations ]🤝
0+Orgs Collaborated With

COMMUNITY ENGAGEMENT

Certifications

Verified credentials in AI, machine learning, and programming.
Because knowledge needs receipts. 🤷

05 CERTIFICATIONS0+ SKILLS
Master Competitive Programming - Complete Beginner to Advanced
Verified
GeeksforGeeks
GeeksforGeeks

Master Competitive Programming - Complete Beginner to Advanced

Explore Credentials
Career Essentials in Generative AI by Microsoft and LinkedIn
Verified
Microsoft / LinkedIn Learning
Microsoft / LinkedIn Learning

Career Essentials in Generative AI by Microsoft and LinkedIn

Explore Credentials
Machine Learning Specialization
Verified
Coursera / Stanford University / DeepLearning.AI
Coursera / Stanford University / DeepLearning.AI

Machine Learning Specialization

Explore Credentials
Java Foundations
Verified
Oracle Academy
Oracle Academy

Java Foundations

Explore Credentials

The modern landscape of software engineering is saturated with transient solutions and superficial integrations. True engineering requires moving beyond the prompt, architecting robust systems where intelligence is deeply embedded into the core logic of the application. It is about understanding the entire stack from vector databases and embedding models to the intricate orchestration of multi-agent workflows.

My methodology

Architectureoverprompts

In an era obsessed with conversational interfaces, the actual differentiator lies beneath the surface. Real value is created through the meticulous design of data pipelines, retrieval mechanisms, and intelligent routing.

SYS_ARCH // ACTIVE

SystemsThinking.RelentlessExecution.

Every feature is a system. Every system is a component of a larger architecture. By adopting a holistic view, we eliminate redundancies and optimize for long-term scalability.

PRECISION // 1:1

Design for Clarity.

Clarity in code translates to clarity in execution. Complexity is an enemy that must be actively fought.

BUILD
INTELLIGENCE,
NOT JUST
FEATURES

The software industry is undergoing a paradigm shift. We are no longer merely programming logic; we are orchestrating cognition. This requires a fundamental departure from traditional MVC architectures towards agentic frameworks where advanced models act as reasoning engines. But reasoning without reliable memory and robust tools is useless.

Therefore, our primary mandate is to construct the scaffolding that allows intelligence to operate safely and effectively within deterministic constraints. Every module must be testable, every prompt must be versioned, and every failure mode must be anticipated. This is the engineering standard of the future, and we are implementing it today.

Measurewhatmatters.

Latency, token efficiency, and retrieval accuracy are the new benchmarks.

PeopleFirst.Always.

Technology exists to serve human needs. We build tools that empower, augment, and respect the individuals who use them.

core values: CLARITY OVER COMPLEXITYLONG-TERM OVER SHORTCUTSIMPACT OVER ACTIVITYLEARN CONTINUOUSLYBUILD WITH INTEGRITY

Contact

Let me know your thoughts
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