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Waqar Makki
@waqarshad897
GenAI Specialist: LLMs, NLP, Computer Vision Expert
Paquistão
Inglês, Urdu
Sobre mim
I am a GenAI-focused Data Scientist & ML Engineer with over 4 years of experience specializing in production-grade NLP, GenAI, and Computer Vision applications. I translate complex R&D into high-impact commercial solutions.
Expertise:
- LLMs & RAG: Architecting AWS pipelines (Bedrock, PGVector) that reduced latency by 30%.
- Computer Vision: Expert in YOLOv8 and high-precision medical image segmentation.
- Agentic Workflows: Engineering autonomous AI ecosystems and REST APIs for rapid response.
I build scalable, optimized AI systems that deliver measurable results. Let’s collaborate!
Habilidades
Waqar Makki
offline •
Conheça meus serviços
Integrações de IA
I will deploy ai agent and workflow automation pipelines
Ajustes de modelo de IA
I will fine tune and deploy custom llms for your specific use case
Experiência profissional
Machine Learning Engineer (GenAI Focus)
NIX Solutions • Período integral
Aug 2025 - Feb 2026 • 6 mos
- End-to-End AI Production: Architected and deployed production-grade REST APIs and LLM systems for automated document parsing and NLP, incorporating comprehensive monitoring and logging. - Scalable MLOps & Cloud Infrastructure: Built high-performance AWS inference pipelines using Bedrock, Lambda, and PGVector, successfully reducing API latency by 30%. - Strategic RAG Optimization: Led RAG prototyping and evaluation using RAGAS, achieving a 22% increase in retrieval precision through benchmarking and executive-level technical demos. - Intelligent Talent Matching Engine: Engineered a multi-stage recruitment platform utilizing Llama 3 70B on AWS Bedrock to automate candidate matching. - Developed hierarchical vector indexes and metadata-rich embeddings to maximize retrieval accuracy. - Implemented dual-pass logic and automated risk detection, significantly reducing manual screening churn. - Autonomous Support Ecosystem: Designed an AI-driven support system integrated with Twilio and Jira, decreasing ticket response times from hours to seconds.
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