Applied AI Engineer
Syed Sajjad Hussain
Applied AI Engineer | GenAI / LLMs | RAG | Fine-tuning | Agentic AI
Open to Global Relocation | Immediate Joiner
Applied AI Engineer and B.Tech CSE graduate focused on LLM product workflows, prompt improvement, RAG systems, AI evaluation, Python applications, and full-stack delivery. Built practical projects across legal document AI, financial analytics, media intelligence, portfolio systems, and client product platforms.
Technical Skills
Generative AI & LLMs: Prompt engineering, Prompt refinement, LLM response evaluation, Response ranking, AI quality assurance, RAG pipelines, LangChain, LangGraph, Vector embeddings, Fine-tuning concepts, LoRA / QLoRA
Python, ML & Data: Python, FastAPI, Streamlit, Pandas, NumPy, Scikit-learn, SciPy, TensorFlow basics, Forecasting, Anomaly detection, Data visualization, PDF/report generation
Databases & Retrieval: ChromaDB, Vector databases, SQLite, PostgreSQL, Document parsing, Semantic search, Cited answer design, Structured data
Full-Stack Development: React.js, Next.js, TypeScript, JavaScript, Vite, Tailwind CSS, REST APIs, Admin CRUD, Responsive UI, Production handoff, SEO-ready structure
Tools & Platforms: Git, GitHub, Streamlit Cloud, OpenAI API, Anthropic Claude API, Hugging Face, Ollama, MCP concepts, Google Search Console
Professional Skills: Remote collaboration, Client communication, Stakeholder updates, Technical documentation, Leadership ownership, Structured feedback, Task ownership, Problem solving, Clear explanation, Product thinking
Experience
Full Stack Developer / Product Platform Developer (Contract) | Econ Building Center / DadiMa Superfood
Jun 2026 - Present
Client product platform, catalog workflow, admin support, and release-ready handoff.
- Built a responsive product catalog platform with categories, variants, stock-style product display, product detail pages, cart-style flow, compliance pages, and distributor/customer CTAs.
- Implemented admin-supported content workflows for managing product and category-style business data.
- Prepared the platform for client review with clean page structure, metadata, release readiness, and business-ready handoff.
Main stack: Frontend engineering, admin CRUD, catalog UX, responsive UI, production handoff
Applied AI Engineer (Contract) | micro1
Apr 2026 - May 2026
Prompt improvement, LLM response quality review, and AI task evaluation.
- Created, tested, and refined prompts to improve LLM response quality, instruction following, clarity, completeness, and task relevance across assigned business/productivity scenarios.
- Reviewed model outputs against task requirements, comparing alternative responses and identifying issues in accuracy, safety, consistency, tone, usefulness, and reasoning quality.
- Provided structured feedback for prompt and response improvement through annotation, response ranking, AI QA, documentation, and deadline-based remote task delivery.
Main stack: Prompt engineering, LLM evaluation, response ranking, data annotation, AI QA
AI Full Stack Developer (Freelance / Remote) | Xbot Startup
Jan 2026 - Present
Client-facing web delivery for business websites, product pages, and conversion flows.
- Delivered responsive client websites and business platforms across interiors, dental, restaurant, ecommerce, real estate, creator, portfolio, and invitation use cases.
- Translated client requirements into production UI, reusable page sections, service/catalog flows, WhatsApp inquiry paths, mobile-first layouts, and deployment-ready pages.
- Worked with a small remote team while handling frontend implementation, testing, client communication, stakeholder updates, and technical handoff.
Main stack: React, Next.js, Vite, TypeScript, JavaScript, Tailwind CSS, responsive frontend delivery
Applied AI Engineer (Remote) | Product R&D
Jan 2024 - Jan 2026
Hands-on AI product development across LLM apps, RAG, analytics, and reporting systems.
- Built practical AI product prototypes for legal document intelligence, financial analytics, and media/content intelligence using Python-first workflows.
- Designed LLM-powered user flows with document parsing, retrieval, prompt orchestration, AI analysis, data visualization, cited outputs, and PDF reporting.
- Focused on applied use cases: reducing manual document review, simplifying financial analysis, and turning long-form media into structured content assets.
Main stack: Python, Streamlit, FastAPI, LangGraph, ChromaDB, Pandas, Plotly, ReportLab
Project Experience
Problem solved: The client needed a trustworthy product platform for food-processing products and distributor/customer inquiries.
Built: Built an admin-supported product catalog with categories, variants, product pages, compliance content, distributor CTA, and responsive production handoff.
Tech stack: Frontend engineering, admin CRUD, product catalog UX, responsive UI, production handoff
Problem solved: Legal documents are long, risk-heavy, and difficult to review manually.
Built: Built a legal document intelligence app with PDF upload, parsing, semantic retrieval, cited answers, page references, and risk flags.
Tech stack: Python, Streamlit, LangGraph, ChromaDB, OpenAI GPT-4o, PyMuPDF
Problem solved: Financial CSVs are hard to analyze quickly without manual spreadsheet work.
Built: Built an AI-assisted analytics app for transaction ingestion, dashboards, forecasting, anomaly detection, financial health scoring, and PDF reports.
Tech stack: Python, Streamlit, Pandas, NumPy, Scikit-learn, Plotly, SQLite, ReportLab
Problem solved: Creators and teams spend too much time extracting useful insights from long-form videos.
Built: Built a media intelligence workflow for transcript analysis, structured summaries, content ideas, social outputs, and prompt-driven analysis.
Tech stack: Python, Streamlit, Anthropic Claude API, yt-dlp, requests, retry handling
Problem solved: Project proof, resume content, writing, and contact paths needed one clear professional system.
Built: Built a searchable portfolio with typed project data, blog, HTML/PDF resume, project detail pages, JSON-LD, Open Graph assets, and fast filtering.
Tech stack: Next.js, React, TypeScript, Tailwind CSS, structured data, performance optimization
Education
B.Tech in Computer Science & Engineering
MAKAUT | 2022-26 | CGPA: 7.8/10 | Graduated June 2026
Certifications & Achievements
National Rank 162 / 18,773, Top 1% in Naukri CodeQuezt #23 | Google Startup School Certified | Academic Excellence: 97/100 Mathematics, 92/100 English in State Boards
Certifications: micro1 Applied AI Engineer | Educosys Live Generative AI Engineering Program | Google for Startups - Startup School: Prompt to Prototype | Coursera / Politecnico di Milano - Machine Learning: an overview