👁️ AI VISION MODE ACTIVATED // NEURAL SYSTEM UNLOCKED
🚨 WARNING: RED EYE RAGE BOSS MODE ACTIVATED! (AUTO-DEACTIVATING IN 10s)
🐜 AI AGENT CRAWLER
MODE
V E D
A
N T
ACTIVE RESEARCH & LIVE DEVELOPMENT
TELEMETRY // ONLINE
LANGGRAPH ARCHITECTURE 88%

Multi-Agent Stateful Graph Execution

Architecting fault-tolerant cyclic DAGs with state persistence, checkpointing, and human-in-the-loop tool approval gates.

TRANSFORMER FINE-TUNING 94%

QLoRA & PEFT Domain Adaptation

Fine-tuning Llama-3 8B & Mistral models on specialized dataset pairs for deterministic SQL construction and structured JSON response schemas.

HYBRID RAG & MLOPS 96%

Sub-50ms Hybrid Vector Search

Deploying hybrid sparse BM25 + dense vector indexing pipelines with Cohere re-ranking on async FastAPI microservice nodes.

INTELLIGENT SYSTEMS ARCHITECT • MACHINE LEARNING • DEEP LEARNING • LARGE LANGUAGE MODELS • AGENTIC AI • LANGCHAIN & LANGGRAPH • PRODUCTION RAG • FASTAPI & MLOPS • CLOUD AI SYSTEMS •
K
A
P I L
AI ENGINEER

Building deterministic reliability into non-deterministic AI intelligence.

EXPLORE NARRATIVE

AB
HELLO, I'M VEDANT KAPIL • AI ENGINEER & ARCHITECT
UT

VEDANT KAPIL

Architecting enterprise-grade AI state machines, autonomous multi-agent graphs, and scalable vector retrieval pipelines.

I am an AI Engineer focused on bridging raw probabilistic models with production-grade deterministic software architecture. My work centers on designing stateful agentic workflows (LangGraph), hybrid RAG pipelines, and low-latency API wrappers using FastAPI and cloud MLOps infrastructure.

I believe true AI engineering is not merely prompting models, but engineering self-correcting feedback loops, robust context retrieval, evaluation harnesses, and resilient systems that execute complex cognitive tasks predictably.

Agentic Graph Execution Hybrid RAG & Vector Search Fine-Tuning & Evaluation MLOps & Microservices

THI
SYSTEM OVER PROMPT • AGENTIC SELF-CORRECTION
KING

Architecting Systems, Not Prompts

A prompt is an opinion; a system is an invariant. Production AI relies on state machines, structured schema enforcement, deterministic fallback paths, and evaluation metrics that run on every iteration.

class AgentState(TypedDict):
    messages: Annotated[list[BaseMessage], add_messages]
    next_node: str
    validation_status: bool

Hybrid Retrieval before Fine-Tuning

Fine-tuning changes parametric memory; RAG provides explicit grounded context. By combining sparse BM25 keyword matching with dense vector embeddings and cross-encoder re-ranking, we achieve zero hallucination with strict source attribution.

Cyclic Agentic Graphs (LangGraph)

Linear LLM chains break easily. Stateful cyclic graphs allow agents to inspect their own tool output, catch syntax errors in code generation, refine queries dynamically, and loop until exact quality criteria are satisfied.

Production MLOps & Async FastAPI

AI models are computationally heavy. Efficient streaming responses, asynchronous execution, model caching, quantization, and real-time observability turn heavy ML models into snappy enterprise microservices.

RES
AI ENGINEER • VERIFIED RESUME & TECHNICAL HIGHLIGHTS
UME

VEDANT KAPIL

AI Engineer & Intelligent Systems Architect

vedantkp79@gmail.com • GitHub: Vedant021004 • LinkedIn: Vedant Kapil

DOWNLOAD RESUME (PDF)

TECHNICAL PROFICIENCIES

Agentic AI & State Machines:

LangChain, LangGraph, Multi-Agent Supervisors, ReAct Framework, Tool Calling, Stateful Graphs

RAG & Vector Retrieval:

Hybrid Vector Search, ChromaDB, FAISS, Pinecone, BM25 Sparse Matching, Dense Embeddings

Machine Learning & Deep Learning:

Python, PyTorch, TensorFlow, Scikit-Learn, Transformer Fine-tuning, Feature Engineering

Backend & MLOps Infrastructure:

FastAPI, Docker, REST APIs, Git, Streamlit, PostgreSQL, Automated CI/CD Pipelines

EDUCATION

Bachelor of Technology in Computer Science & Engineering
Specialization in Artificial Intelligence & Machine Learning

FEATURED AI PROJECTS

Amazon Product Catalog RAG Pipeline RAG / LangChain

Engineered an end-to-end Hybrid RAG product retrieval system combining dense semantic embeddings with BM25 keyword matching for e-commerce query resolution with 94%+ precision.

AI Data Analyst Copilot LangGraph / Agents

Architected an autonomous SQL-generating data analyst agent utilizing LangGraph state machines, multi-step query validation, and dynamic chart generation.

Smart School Management AI System Full-Stack AI

Built an automated academic management platform incorporating intelligent student progress tracking, automated report generation, and NLP query interfaces.

Traffic Prediction ML Model Machine Learning

Developed time-series machine learning models predicting urban traffic congestion patterns using historical flow telemetry with PyTorch and Scikit-Learn.

PORTF
5 ENGINEERING CASE STUDIES // REAL AI BLUEPRINTS
LIO

FUTUR
AUTONOMOUS MULTI-AGENT DAGS • EDGE LLMS
E

01

Autonomous Agentic Frameworks

Moving beyond single-agent LLM invocations to hierarchical, self-healing multi-agent DAGs that autonomously write, test, evaluate, and deploy software modules.

02

Enterprise Cognitive Knowledge Graphs

Fusing structured Knowledge Graphs with unstructured vector databases to give AI models enterprise-wide long-term memory with mathematical accuracy.

03

Local Edge LLM Inference

Optimizing small language models (SLMs) via quantization and hardware acceleration to run highly capable AI agents privately at low latency on edge infrastructure.

C
HOW CAN I HELP? // LET'S BUILD INTELLIGENT SYSTEMS
NTACT

LET'S BUILD INTELLIGENT SYSTEMS TOGETHER

Interested in collaborating on production Agentic AI, RAG pipelines, or ML engineering infrastructure?

SEND DIRECT MESSAGE
VK-AI COPILOT // REASONING ENGINE v2.4
VK-AI: Hello! I am Vedant Kapil's synthetic assistant. Ask me anything about Vedant's projects, RAG architecture, or AI engineering background.
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