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AI/ML Engineer | Data Scientist | NLP • Speech AI • Clinical AI | Healthcare & Defense
AI/ML Research Engineer and Data Scientist with 7+ years of experience building production-grade machine learning systems across healthcare, speech, NLP, and computer vision. I specialize in end-to-end ML pipelines — from multimodal model development and MLOps to clinical interpretability and real-world deployment. My work includes multilingual speech AI for underserved patient populations, ASR and NLU systems for defense applications, and biomedical signal processing research published in IEEE Transactions. I hold an M.Sc. in Computer Engineering from UMBC. Currently open to Data Scientist and AI/ML Engineer roles, with a strong interest in health-tech and digital health.
University of Maryland Baltimore County
Master’s Degree, Computer Engineering
January 1, 2015 – January 1, 2017
Amirkabir University of Technology - Tehran Polytechnic
Master’s Degree, Computer Engineering
N/A – Present
University of Tehran
Bachelor’s Degree, Computer Hardware Engineering
N/A – Present
HealthTequity
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Mindboard, Inc.
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UMBC-Department of Computer Science and Electrical Engineering
Research Assistant
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Baltimore, MD · On-site
PARMAN
Digital Design Engineer
May 1, 2009 – August 1, 2013
Tehran, IRAN
Neural Network and Deep Learning
Coursera
June 24, 2026 – Present
Data science fellowship
The Data Incubator
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse experience across defense, healthcare, and finance sectors, coupled with academic research, indicates adaptability and a broad interest in applying ML to various domains. The focus on ethical AI (bias-free, reliable clinical outputs) aligns with responsible AI development practices. However, the lack of explicit team collaboration or leadership roles in some descriptions makes it difficult to fully assess cultural fit beyond technical contributions.
Soft Skills & Operational Fit
The candidate's experience descriptions suggest strong problem-solving skills, a collaborative approach (e.g., 'Collaborated on cross-functional R&D projects'), and a focus on rigorous evaluation and governance in AI systems. The detailed project outcomes indicate a results-oriented mindset.