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Ashton Mehmood

@ashtonmehmood

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Senior AI/ML Engineer at Rexit building production RAG pipelines and autonomous agentic workflows for enterprise AI.

United States

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At Rexit, I lead the architecture and implementation of enterprise AI platforms, including production RAG pipelines with hybrid retrieval and custom reranking. I also architect multi-agent workflows and deploy low-latency LLM services using quantization and vLLM.

Previously, at HFS, I built and deployed machine learning solutions from exploratory data analysis through production, including real-time inference services across AWS and Azure. At Clarika, I developed data pipelines, predictive models, and NLP applications; my projects also include a document-based RAG assistant and healthcare document classification.

Experience

Work history, roles, and key accomplishments

Current

Senior AI/ML Engineer

Current

Rexit

Aug 2024 - Present (2 years 2 months)

Lead end-to-end technical architecture and implementation of enterprise-grade AI and ML production platforms. Direct engineering of production RAG pipelines and autonomous agentic workflows, while mentoring senior engineers and overseeing MLOps lifecycle practices.

Python PyTorch TensorFlow Lang Chain LangGraph RAG Vector Search LLM Fine Tuning PEFT LoRAs QLoRA VLLM Quantization AWQ GPTQ MLFlow CI CD Docker Kubernetes AWS Azure Databricks Model Monitoring Data Drift RAG Triad fastAPI

Senior Machine Learning Engineer

HFS

Apr 2021 - Jul 2024 (3 years 3 months)

Took end-to-end technical ownership of machine learning solutions from EDA to deployment. Built and optimized deep learning models, containerized real-time inference microservices, and established experiment tracking and model registry workflows.

Python PyTorch TensorFlow scikit learn fastAPI Docker Kubernetes AWS Azure MLFlow Optuna Onnx PySpark Pytest LLM Transformers NLP

AI/ML Engineer

Clarika

Jan 2018 - Feb 2021 (3 years 1 month)

Built foundational data processing pipelines and EDA routines using Python, Pandas, NumPy, and SQL. Developed and trained machine learning models, implemented NLP and deep learning models, and deployed models as REST API endpoints.