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AI Engineer with 3+ years in LLMs, Agentic AI, and Computer Vision
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Results-driven AI Engineer with around 2 years of experience architecting and deploying production-grade Large Language Models (LLMS), Agentic workflows, and Computer Vision systems. Proven track record of optimizing AI pipelines for latency and scalability, integrating modern frameworks like LangGraph and MCP, and building robust backend microservices to deliver high-impact enterprise AI solutions.
National University of Science & Technology (NUST)
Bachelor of Software Engineering · Software Engineering
N/A – June 30, 2026
Zayup Communications
AI Software Engineer & Technical Lead
November 1, 2024 – Present
Catonsville, Maryland, United States
Freelance (Upwork & Fiverr)
AI Solutions Developer
July 1, 2024 – Present
India
Mach Vis Lab, SEECS, NUST
Deep Learning Intern
June 1, 2024 – August 31, 2024
Islamabad, Islamabad Capital Territory, Pakistan
GPT Collab Fn Creator
December 1, 2024 – June 30, 2026
• Contributed to an AI function creator, implementing a complex agentic loop that achieved a ~2% score on SWE-bench tasks utilizing GPT-40 mini, LangGraph, and Elasticsearch. • Engineered a Human-in-the-Loop (HIL) mechanism utilizing WebSockets for real-time validation.
AI Voice Ordering Agent (RP2A)
October 1, 2024 – June 30, 2026
• Engineered a scalable custom voice agent SDK built on LiveKit, integrating Deepgram, ElevenLabs, and Kokoro TTS, improving live order processing efficiency and cost reduction by 75%. • Slashed MCP tool call latency by 75%+ (from 2-4s to <1s) by geographically co-locating databases, implementing in-memory caching (Redis), and resolving ORM N+1 issues via eager loading. • Architected a multi-vendor restaurant CRM MCP server (FastMCP, FastAPI) with ChromaDB, boosting menu retrieval accuracy using BM25, semantic search, and RRF/MMR ranking strategies.
AutoSynth (Synthetic Data Framework)
August 1, 2024 – September 30, 2024
• Developed a scalable data engineering framework that generates synthetic data leveraging SOTA techniques (Magpie, Agent Instruct, Genstruct) alongside open and closed-source LLMs. • Utilized Distilabel and LangChain for pipeline execution and implemented advanced prompting techniques including Chain-of-thought, ReAct, and Monte Carlo tree search.
Ignitic AI (Multi-Agent E-Commerce Platform)
June 1, 2024 – June 30, 2026
• Architected a microservices-based e-commerce "super-agent" using Go (Gin) and Python (FastAPI), utilizing RabbitMQ to decouple time-intensive LLM tasks and maintain high frontend responsiveness. • Developed 12 specialized AI agents via LangGraph and Model Context Protocol (MCP), successfully integrating 63 dynamic external tools to automate customer support and product research. • Designed a scalable polyglot persistence layer (PostgreSQL, MongoDB, Redis) and established a Kubernetes CI/CD pipeline, securely managing over 1,000+ complex agent runs with AES-256-GCM encryption.
Automated Computer Vision OCR Pipeline
June 1, 2024 – July 31, 2024
• Fine-tuned YOLOv8n on a 23k-image dataset, achieving a 92% mAP; applied OpenCV augmentation to improve robustness by 18% and integrated TR-OCR for high-accuracy text extraction. • Built an asynchronous pipeline utilizing DeepSort, capable of processing 30+ FPS in real-time.
View ProjectIBM RAG and Agentic AI Professional
Coursera
January 1, 2026 – Present
Information Security Fundamentals
Cisco
January 1, 2025 – Present
Data Analysis
January 1, 2023 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, ranging from voice agents and e-commerce platforms to synthetic data generation and computer vision, indicates a broad interest and adaptability, which is a strong cultural fit for dynamic AI roles. Their experience in both contract and freelance capacities, alongside an internship, shows a willingness to engage in various work environments. The continuous pursuit of certifications (IBM RAG and Agentic AI Professional, Information Security Fundamentals) demonstrates a commitment to continuous learning and staying current with industry trends, aligning well with an innovative and growth-oriented culture. The collaborative nature of projects (e.g., 'GPT Collab Fn Creator') also suggests good teamwork potential.
Soft Skills & Operational Fit
The candidate demonstrates strong project management and leadership skills through their role as an AI Software Engineer & Technical Lead, where they managed a team and maintained high velocity using agile methodologies. Their freelance work highlights client communication and problem-solving abilities, translating complex business logic into scalable AI microservices. The focus on optimizing performance and cost reduction in projects indicates a results-driven and efficient operational approach. The 'Human-in-the-Loop' mechanism in a project also suggests an understanding of robust system design and validation.