Muhammad Asif

Generative AI Engineer with 2+ years building and shipping production LLM systems from fine-tuning transformer models (LoRA/QLoRA) to deploying multi-agent pipelines on AWS.

Experienced across the full GenAI stack: RAG architectures, vector databases, LangChain/LangGraph orchestration, and containerized ML deployment. Delivered measurable outcomes including a 70% reduction in manual review time and 88% average ATS compatibility on an AI-powered resume tool. Seeking a high-impact GenAI engineering role at a startup or product company.

Generative AI Engineer at AiTechFusion (2024-09 – 2025-12)

Designed and optimized Advanced RAG pipelines, engineered multi-agent AI systems, fine-tuned transformer models, and deployed production services on AWS.

• Designed and optimized Advanced RAG pipelines using vector databases (ChromaDB, Pinecone, FAISS), improving data retrieval accuracy and reducing query latency for large document corpora
• Engineered stateful, multi-agent AI pipelines utilizing LangGraph for workflow routing and LangChain for tool integration, enabling LLMs to execute complex reasoning tasks autonomously
• Fine-tuned and optimized transformer-based LLMs for NLP tasks such as Q/A, Sentiment Analysis, and domain Specific Text Generation, improving model efficiency and task accuracy
• Developed end-to-end ML pipelines (data ingestion → Preprocessing → training → deployment → monitoring), enabling continuous model evaluation and performance tracking
• Containerized and deployed 4 production AI services on AWS EC2 via Docker(ECR) + GitHub Actions CI/CD, reducing deployment time from manual hours to under 10 minutes per release

Generative AI Intern at Insafdar (2024-07 – 2024-08)

Developed NLP solutions for legal case management including sentiment analysis and document classification.

• Developed NLP solutions for legal case management, including sentiment analysis for client feedback classification and automated document classification using machine learning techniques
• Explored LLM applications (GPT, BERT) for intelligent legal research and developed sentiment analysis tooling for client feedback processing

Bachelor of Science in Artificial Intelligence – Bahria University