LLM product thinking
I connect models, prompts, retrieval, UI, and evaluation into usable workflows.
Syed Sajjad Hussain
Applied AI Engineer building LLM, RAG, and evaluation systems.
I build practical AI workflows that connect prompts, retrieval, model evaluation, fine-tuning concepts, Python services, and product-grade interfaces.
Applied AI EngineerLLM Apps / Prompt EngineeringRAG & Vector DatabasesFine-tuning / LoRA ConceptsPython + FastAPI + Streamlit
2.5+ years
Hands-on delivery
AI products, client platforms, and remote delivery
B.Tech CSE
Education
MAKAUT | 2022-26
3 live AI systems
AI product portfolio
LexiQ, FinanceIQ, and SaveIQ
AI + Web
Client-ready work
RAG, analytics, catalogs, and case studies
Profile
AI engineering with product and delivery context.
My work is centered on practical LLM applications, document workflows, analytics interfaces, evaluation tasks, and full-stack websites. The goal is simple: build systems that can be reviewed, used, and improved.
Python-first AI engineering
Comfortable with Streamlit, FastAPI, data pipelines, ML libraries, and LLM APIs.
Full-stack delivery
Can build hiring-team demos, client websites, admin flows, dashboards, SEO pages, and deployment pipelines.
Clear communication
Experienced with remote collaboration, documentation, prompt review, client needs, and technical explanations.
Selected Projects
Live work, code links, and case studies.
A focused set of AI and full-stack projects with clear problem, solution, stack, and links.
Capabilities
Skills connected to the work that demonstrates them.
The portfolio avoids long badge lists. Each key skill points to a project, contract, or training artifact.
Generative AI & LLM Engineering
LLM product workflows, retrieval, prompting, and evaluation.
Python / ML / Data
Python-first AI apps, analytics, and reporting workflows.
Full Stack / Web
Modern web apps, client platforms, admin flows, and SEO.
Tools / Platforms
Repository, indexing, QA, and performance tooling.
Professional Strengths
The collaboration layer that makes technical work usable.
Experience
AI product R&D, evaluation work, and client delivery.
Concise timeline of practical AI, LLM evaluation, full-stack websites, and SEO-oriented business platforms.
01Remote
Jan 2024 - Jan 2026
Applied AI Engineer (Remote)
Product R&D
Built practical AI product prototypes while deepening GenAI, LLM, RAG, analytics, and Python engineering skills through market-oriented projects.
PythonLLM AppsRAGLangGraphChromaDBStreamlitPrompt Engineering
02Remote / International client work
Jan 2026 - Present
AI Full Stack Developer (Freelance / Remote)
Xbot Startup
Worked in a small 3-person international setup delivering client websites, SEO-structured business pages, WhatsApp automation flows, product catalogs, and deployment support.
ReactVite/Next.jsTypeScriptJavaScriptResponsive UISEOProduction Handoff
03Remote
Apr 2026 - May 2026
Applied AI Engineer (Contract)
micro1
Supported AI development workflows through prompt improvement, LLM response evaluation, response ranking, data annotation, structured feedback, and quality assurance.
Prompt EngineeringGenerative AILLM EvaluationAI TrainingAI Quality AssuranceData AnnotationRemote Collaboration
04Remote / Client project
Jun 2026 - Present
Full Stack Developer / SEO Engineer (Contract, Ongoing)
Econ Building Center / DadiMa Superfood
Built and deployed a full-stack e-commerce style platform for DadiMa Superfood with admin support, product catalog structure, CRUD workflows, SEO content, and technical SEO readiness.
Full Stack DevelopmentE-commerce UIAdmin CRUDSEO ContentTechnical SEOProduction HandoffResponsive Design
Client Work
Business websites and commerce-style interfaces.
Selected delivery work across service pages, product catalogs, WhatsApp flows, mobile layouts, and technical SEO structure.
Blog
Story-led AI notes with practical takeaways.
Clear posts on RAG, document AI, analytics, prompt design, evaluation, and working AI products.
2 min read
RAG in Simple English: The Moment a Chatbot Learns to Check the File
A clear story about retrieval augmented generation, why it matters, and how it changes a chatbot from guessing to looking things up.
RAG is not magic. It is a disciplined way to let an AI answer from the right documents instead of relying only on memory.
2 min read
Fine-Tuning vs RAG: The Builder's First Fork in the Road
A practical decision story for builders choosing between training a model and giving the model better context.
Start with RAG when the problem depends on fresh documents. Consider fine-tuning later when the behavior itself needs to change.
2 min read
How I Built LexiQ: From Long Legal PDFs to Cited AI Answers
A build story about turning dense legal documents into a usable RAG workflow with citations, risk flags, and careful output design.
The hardest part of legal AI is not calling a model. It is making the answer inspectable, cautious, and useful.
2 min read
What FinanceIQ Taught Me About Making AI Analytics Understandable
Lessons from building a transaction analytics app that turns CSVs into forecasts, anomalies, charts, and reports.
AI analytics works best when the interface first makes the data structure obvious.