AI Engineer with 1+ years in RAG, LLM Integration & Full-Stack Development
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AI/ML Engineer and Full-Stack Developer with 1+ year of professional experience designing and shipping production-grade intelligent systems. Deep expertise in Retrieval-Augmented Generation (RAG), LLM integration, vector databases, semantic search, and AI agent architecture. Proven track record building multi-tenant AI SaaS platforms, hybrid search engines (dense + BM25), automated NLP pipelines, and browser-based AI automation tools. Skilled in Python, FastAPI, and the MERN stack; experienced with Docker, Kubernetes, and cloud deployment. Currently architecting an AI-powered advertising creatives generation platform leveraging Fal AI, Kling, and Remotion on DigitalOcean Kubernetes.
Islamia University of Bahawalpur, Pakistan
Bachelor of Science · Information Technology
August 1, 2022 – June 30, 2026
Jobalize
AI/ML & Full-Stack Developer
January 1, 2025 – Present
India
Orbion Connect
Backend & RAG Developer (Contributor)
January 1, 2025 – Present
India
CleanVibe
Backend Developer
January 1, 2025 – Present
India
AI-Powered Advertising Creatives Generation Platform
January 1, 2026 – Present
Architecting a Kubernetes-based (DigitalOcean) platform that auto-generates ecommerce ad creatives using Fal AI, Kling, Seedance, and Remotion with Claude Sonnet as the orchestration LLM. Designed a Job-per-request K8s pattern with dispatcher services and async handoff for long-running video generation jobs. Implemented MCP protocol mechanics and skills-as-thin-wrappers design for modular AI pipeline orchestration.
AI-Powered Micro SaaS RAG Document Querying System
January 1, 2025 – January 1, 2026
Built a full-stack multi-tenant SaaS platform enabling authenticated users to query PDF, DOCX, TXT, and CSV documents via natural language using RAG architecture. Engineered an automated document ingestion pipeline: text extraction ← 512-token overlap-aware chunking → 384-dimensional vector embedding via SentenceTransformers. Integrated OpenAI GPT and HuggingFace LLMs for grounded answer generation with source chunk citations to minimize hallucination. Achieved 97% test pass rate across 33 test cases; 3.2s average query response with verified support for 75 concurrent users. Stack: FastAPI, React.js, PostgreSQL, ChromaDB, Docker, JWT.
Intelligent AI Automation Agent System
January 1, 2025 – January 1, 2026
Designed and built a production-grade AI automation system (Python + FastAPI) with a layered Input / AI Processing / Automation Engine / Output architecture. Integrated n8n as the workflow orchestration layer with visual trigger-process-action pipelines connecting the LLM agent to external APIs and data sources. Built automation pipelines for email processing, data extraction, and automated report generation using Celery task scheduling. Developed a lead generation module: scrapes structured data, cleans with Pandas, and exports to CSV/JSON for downstream ML pipelines.
NestJS / Gemini Resume Optimization Service
January 1, 2025 – January 1, 2026
Built an ATS scoring service modeled on Jobscan's methodology using NestJS and Google Gemini, with algorithmic keyword extraction and skill gap analysis.
Cultural Fit Analysis
The candidate's project diversity, ranging from AI-powered advertising to RAG document querying and automation agents, indicates a broad interest in applying AI across different domains. The experience in building multi-tenant SaaS platforms and contributing to job matching engines aligns well with product-focused, innovative environments. The listed academic projects also show initiative and a drive to apply learned concepts. However, the candidate's experience level is relatively low (1+ year), and all professional experiences listed start in 2025, which is in the future, making it difficult to fully assess real-world cultural fit based on past team dynamics or long-term project commitments. The education is still ongoing, which might impact immediate full-time availability or commitment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex system design (e.g., Job-per-request K8s pattern, hybrid search engines). Project descriptions indicate an ability to work on end-to-end solutions, from architecture to deployment. The focus on production-grade systems and performance metrics (e.g., 97% test pass rate, 3.2s average query response) suggests an attention to quality and operational excellence. However, without specific psychometric or English test results, a comprehensive assessment of work attitude, stress handling, and team collaboration is not possible.