Building IntelligentSystems That Think,Learn & Scale.
AI/ML Engineer specializing in Generative AI, Agentic AI, RAG, Computer Vision, MLOps and AWS Cloud Engineering.
Engineering intelligence, end to end.
I'm Abrar Ahmad — an AI/ML Engineer and AWS Cloud Engineer who bridges the critical divide between experimental machine learning models and scalable, production-grade cloud software.
My expertise centers around the frontier of Generative AI (autonomous multi-agent workflows, hybrid RAG with vector retrieval, and contextual reasoning) paired with real-time Computer Vision (detection, tracking, and automated safety systems using YOLOv8 and PyTorch).
Rather than isolated demos, I build complete systems: from data pipelines and model optimization to containerized FastAPI microservices and fault-tolerant cloud infrastructure on AWS.
Generative & Agentic AI
Enterprise RAG pipelines, autonomous multi-agent teams, and LLM applications that reason over proprietary knowledge bases with deterministic tool-calling precision.
Computer Vision & Safety
Real-time detection, tracking, license plate recognition, and industrial safety systems engineered with YOLOv8, OpenCV, and PyTorch for edge and cloud deployment.
High-Performance ML Backends
Production-ready async Python and FastAPI microservices that serve models with ultra-low latency, strict response validation, and modular architecture.
MLOps & AWS Cloud Engineering
Deploying, containerizing with Docker, and scaling intelligent systems on AWS (ECS, Lambda, S3, EC2) backed by automated CI/CD and observability.
How I architect intelligent systems.
Core principles guiding every system from initial model formulation to production rollout.
Production Over Prototypes
Moving beyond quick notebooks into containerized, low-latency microservices engineered for high concurrency and real-world reliability.
Autonomy With Strict Guardrails
Architecting agents and RAG workflows with structured output validation, hallucination checks, and reliable fallback loops.
Cloud-Native & Cost-Optimized
Designing resilient AWS infrastructure that scales horizontally under load while actively controlling token consumption and inference costs.
Five domains that combine into systems which perceive, reason and scale in production.
Generative AI
Building applications powered by large language models and generative systems.
Agentic AI
Designing autonomous agents that reason, plan and use tools to complete tasks.
RAG
Retrieval-Augmented Generation grounding LLM answers in a knowledge base.
LangChain
Orchestrating LLM chains, retrievers and tools into production pipelines.
LLM Applications
End-to-end apps built on top of hosted and open large language models.
AI Agents
Multi-step agents that combine reasoning with external APIs and memory.
Nexus Bids AI — Autonomous Multi-Agent RFP Bidder
An enterprise-grade autonomous multi-agent AI system that parses RFPs, evaluates Go/No-Go feasibility, retrieves institutional knowledge via ChromaDB RAG, and generates production-ready technical proposals and cost estimates.
Enterprise RAG Chatbot
A cost-effective Retrieval-Augmented Generation AI chatbot that crawls website content, converts the knowledge into vector representations and uses that knowledge base to provide context-aware answers.
CareBot — AI Healthcare Assistant
An AI assistant built around hospital information that provides fast answers related to hospital policies, doctor schedules, OPD timings and appointment information.
AI Digital Twin
An AI-based digital twin concept designed to represent a user's knowledge, personality and conversational style.
AI Resume Roaster & ATS Optimizer
An AI-powered resume critique and ATS optimization engine that parses PDFs, roasts cliché buzzwords, scores formatting readability, and rewrites weak bullets using Groq LLaMA 3 and STAR methodology.
Car Number Plate Detection
YOLOv8-based computer vision system for detecting vehicle number plates in images and video.
From raw data to deployed intelligence.
Every system I ship follows a disciplined, production-grade path — combining modern AI models, rigorous evaluation, and resilient AWS cloud infrastructure.
Autonomous multi-agent orchestration, hybrid semantic retrieval, and tool-augmented generation with zero-hallucination verification loops.
COGNITIVE ARCHITECTUREIngest & Chunk
Parse unstructured PDFs, tables, and docs with semantic chunking, extracting high-dimensional embeddings into vector databases.
Hybrid Retrieval
Execute two-stage hybrid retrieval combining dense semantic similarity with BM25 keyword matching and cross-encoder re-ranking.
Agent Reasoning
LangGraph orchestrates autonomous sub-agents with dynamic tool calling, structured Pydantic schemas, and citation verification.
FastAPI & AWS Serve
Expose high-throughput streaming endpoints with async FastAPI, containerized in Docker and autoscaled across AWS ECS Fargate.
Open to AI/ML engineering, Cloud Computing roles, freelance work and collaborations on ambitious systems. Reach out through any channel below.