Muhammad Jawad

AI Engineer

https://www.opentalent.in/muhammad-jawad

AI Engineer with less than a year in LLM & RAG Systems with expertise in Python backend development.

Key Strengths

Strong foundational knowledge in Generative AI concepts including RAG pipelines, multi-agent systems, and model distillation.
Demonstrated practical experience with FastAPI for backend development and Python for AI/ML applications.
Experience with vector databases (ChromaDB, PostgreSQL for metadata) and prompt engineering.
Exposure to full-stack development (Django, React) for end-to-end AI feature delivery.
Project experience aligns well with the 'AI Engineer' target role, showcasing relevant skills and project types.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's academic projects demonstrate a strong interest and initiative in cutting-edge AI technologies. The diversity of projects, from on-device AI to RAG systems and multi-agent orchestration, indicates a broad technical curiosity and willingness to explore different facets of AI engineering. The focus on privacy-first design (MediNova) suggests an awareness of ethical considerations in AI. However, without professional experience, assessing cultural fit beyond technical alignment is challenging.

Soft Skills & Operational Fit

The candidate's project descriptions indicate an ability to work on complex, multi-component systems (e.g., full-stack health assistant, multi-agent systems). The mention of 'Agile/Scrum exposure' suggests familiarity with modern development methodologies. However, without specific work experience or psychometric test results, it is difficult to fully assess soft skills like teamwork, stress handling, or communication clarity in a professional setting.

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About

Generative AI Engineer with hands-on experience building LLM-based systems: RAG pipelines, multi-agent orchestration, and on-device model distillation. Strong Python foundation across FastAPI backends, vector database design, and prompt engineering, with practical exposure to full-stack delivery (Django, React) for shipping AI features end-to-end. Comfortable working with LangChain, LangGraph, and cloud-deployable architectures.

Top Skills

FastapiPydanticLangchainLanggraphRag Pipelines

Projects

On-Device AI Health Assistant (MediNova)

June 1, 2025 – June 1, 2026

Built a full-stack health assistant with a FastAPI backend, structured data pipeline, and local LLM inference layer for offline mobile use. Distilled a lightweight medical LLM from BioMistral into a smaller Qwen model to maintain strong performance under mobile hardware constraints. Architected a privacy-first, no-cloud system processing all report analysis and health suggestions entirely on-device, aligned with HIPAA/GDPR standards. Implemented a backend pipeline to parse and interpret lab report data, returning structured insights and personalized recommendations.

AI Document Intelligence System (RAG Pipeline)

June 1, 2025 – June 1, 2026

Built an end-to-end Retrieval-Augmented Generation system for querying PDF and text documents, including document chunking, embeddings, and semantic search. Integrated PostgreSQL for metadata storage and query tracking, and designed prompts to return structured, reliable outputs from the LLM.

View Project

Multi-Agent Workflow & Conflict Detection System

June 1, 2025 – June 1, 2026

Developed a multi-agent orchestration system with conditional routing, tool selection, and state management across agent executions. Demonstrated practical reasoning pipelines and automated decision workflows for real-world task scenarios.

View Project

Blog Platform - Full Stack Web Application

June 1, 2025 – June 1, 2026

Built a multi-app Django web application with content management, an admin dashboard, and database models/views handling blog post creation and rendering through Django's ORM.

View Project

Key Strengths

Strong foundational knowledge in Generative AI concepts including RAG pipelines, multi-agent systems, and model distillation.
Demonstrated practical experience with FastAPI for backend development and Python for AI/ML applications.
Experience with vector databases (ChromaDB, PostgreSQL for metadata) and prompt engineering.
Exposure to full-stack development (Django, React) for end-to-end AI feature delivery.
Project experience aligns well with the 'AI Engineer' target role, showcasing relevant skills and project types.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

Muhammad Jawad

Key Strengths

Cultural & Operational Fit

About

Top Skills

Skills

Education

Projects

Key Strengths

Cultural & Operational Fit