Open to opportunities

Mason Iqbal

@masoniqbal

Message

Senior Generative AI engineer building scalable LLM and RAG systems that improve accuracy and reduce latency.

United States

Message

What I'm looking for

I’m looking for a team building production LLM/RAG products where I can own end-to-end delivery—improving accuracy, reducing latency and cost, and strengthening reliability with MLOps, monitoring, and automated retraining.

I’m a Senior Generative AI and Machine Learning Engineer with 8+ years of experience building and scaling production-grade AI systems. I focus on Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and end-to-end ML pipelines—translating business requirements into measurable impact in high-scale environments.

In recent roles, I designed and deployed LLM-based applications using RAG pipelines, improving information retrieval accuracy by 30% and building AI assistants that support thousands of users with sub-200ms response times. I’ve also implemented vector search systems (FAISS/Pinecone), reduced deployment time by 40%, optimized inference with quantization and batching (cutting compute costs by 25%), and strengthened reliability through CI/CD, automated retraining, and model monitoring.

Experience

Work history, roles, and key accomplishments

Current

Senior Machine Learning Engineer

Current

Istream Solution

Jan 2023 - Present (3 years 5 months)

Designed and deployed LLM-based RAG applications, increasing information retrieval accuracy by 30% and improving query performance by 35%. Built end-to-end ML pipelines, cutting deployment time by 40%, and reduced inference compute costs by 25% through quantization and batching.

LLM Retrieval Augmented Generation (RAG)Prompt Engineering Faiss Pinecone AWS Docker Kubernetes Vector Search

AI/ML Engineer

Streamline Analytics

May 2020 - Dec 2022 (2 years 7 months)

Developed fraud detection models using XGBoost and LightGBM, improving detection accuracy by 25%, and built real-time Kafka streaming pipelines that reduced processing latency by 50%. Deployed ML models as REST APIs and implemented CI/CD with automated retraining, while prompt engineering and fine-tuning improved LLM response accuracy by 25%.

XGBoost LightGBM Kafka REST APIs CI CD Prompt Engineering Fine Tuning

Machine Learning Engineer

NextEra Analytics

Aug 2017 - Apr 2020 (2 years 8 months)

Built healthcare NLP systems for entity recognition, improving accuracy by 20%, and developed predictive models for patient risk scoring and analytics. Designed secure, modular ML architectures and pipelines to improve maintainability by 20% and reduce API response times by 30% using caching and asynchronous processing, supported by A/B testing.

NLP predictive modeling BERT Data Pipelines HIPAA compliance A B Testing Caching Asynchronous