Open to opportunities

Anthony Moza

@anthonymoza

Message

Principal AI engineer building production GenAI, NLP, and vision systems that users validate and adopt.

Pakistan

Message

What I'm looking for

I’m looking for a team where I can build production-ready GenAI and ML systems end-to-end—partnering with product and design, running MLOps, and shaping AI-driven UX that delivers measurable, user-validated outcomes.

I’m a principal-level AI Engineer with 5+ years of experience designing and deploying intelligent, user-centered AI systems across GenAI, NLP, computer vision, and data engineering. I focus on turning complex AI capabilities into intuitive workflows people can actually use.

I’ve engineered production-grade LLM/GenAI pipelines, cutting inference latency by over 30% through model quantization and optimized serving strategies. I also lead requirements-gathering workshops to define AI solution scope, timelines, and user experience goals, then translate outcomes into accessible UX and rapid prototypes.

From end-to-end MLOps (model versioning, Dockerized deployment, CI/CD) to building NLP-powered FastAPI services, I bridge technical depth with stakeholder clarity using journey mapping, visual storytelling, and REST APIs. I’m passionate about production-ready solutions that deliver meaningful, user-validated impact.

Experience

Work history, roles, and key accomplishments

Current

Lead AI/ML Engineer

Current

RetroX Interactive

Jan 2023 - Jun 2026 (3 years 5 months)

Developed and maintained NLP-powered backend services with FastAPI to process and analyze text for internal decision-making workflows. Built and fine-tuned Hugging Face Transformer pipelines to improve entity extraction and classification accuracy, and delivered REST APIs integrating ML models into production systems.

NLP fastAPI Model Fine Tuning Text Classification Systems REST APIs Prompting Concepts For NLP Workflows

Software Engineer (AI/ML)

Devsinc

Jan 2021 - May 2022 (1 year 4 months)

Engineered and deployed production-grade LLM/GenAI pipelines, reducing inference latency by over 30% through model quantization and optimized serving strategies. Led stakeholder workshops and translated AI capabilities into intuitive, accessible user workflows while managing the end-to-end MLOps lifecycle with Docker and CI/CD.

Model Quantization MLOps Model Versioning Docker