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AI Engineering Lead | Principal Engineer | Creating Business Impact with Responsible AI
At Deloitte Australia, my role as a Specialist Director is marked by fostering a cutting-edge AI capability expanding our machine learning and AI offerings. Our team's efforts are concentrated on integrating AI to redefine business workflows, positioning us at the forefront of an industrial revolution. With a focus on human-machine collaboration, I am building a formidable cross-functional capability that sets industry benchmarks. My tenure has been characterized by mentoring a new generation of ML engineers and data scientists, aiming to equip them with the skills necessary to excel in a data-driven future. This dedication to empowerment is rooted in my own 20 years of experience and technical expertise in AI, Gen AI, ML and NLP.
University of Sydney
PhD scholar, Information Technology
January 1, 2007 – January 1, 2008
Amirkabir University of Technology - Tehran Polytechnic
Doctor of Philosophy (Ph.D.), Electrical (Biomedical) Engineering
January 1, 2003 – January 1, 2008
Amirkabir University of Technology - Tehran Polytechnic
Master’s Degree, Electrical (Biomedical) Engineering
January 1, 2000 – January 1, 2003
Shahid Beheshti University
Bachelor's degree, Biomedical Engineering
January 1, 1996 – January 1, 2000
Westpac Group
AI Engineering Lead
November 1, 2025 – Present
Sydney, New South Wales, Australia · Hybrid
Deloitte Australia
Specialist Associate Director
October 1, 2020 – Present
Sydney, New South Wales, Australia
Accenture Australia
ML/NLP Lead in AI practice
August 1, 2017 – September 1, 2020
Sydney, Australia
Tyde health
Lead Machine Learning and NLP Data Scientist
June 1, 2016 – January 1, 2017
Greater Sydney Area
Appen
Specialist in Computational Linguistics
April 1, 2016 – August 1, 2017
Sydney, Australia
University of Sydney
Lecturer in Data Analytics
August 1, 2015 – June 1, 2017
Greater Sydney Area
Sharif University of Technology
Assistant Professor- Department of Computational Linguistics
May 1, 2010 – May 1, 2017
Tehran Province, Iran
University of Sydney
Research Fellow- Language Technology Laboratory
May 1, 2007 – October 1, 2008
Greater Sydney Area
Amirkabir University of Technology - Tehran Polytechnic
Research Fellow- Speech and Language Technology Laboratory
January 1, 2005 – March 1, 2010
Tehran Province, Iran
Design and implementation of a text comprehension and summarization system inspired by human mind
January 1, 2015 – Present
In this project we designed a Persian text summarization system based on the most verified human text comprehension models. The system mostly extracts causality relations between entities and events to generate a summary.
Extracting mental models, individual or a team, through cognitive-linguistics processing of text and speech
January 1, 2015 – Present
In this project we extracted cognitive and linguistics features from text material of writings and speeches of well-known people to form their mental model representing their thoughts, beliefs and attitudes toward different subjects. We also define mathematical metrics to measure how similar are mental models of different people. The features we used have been defined in various linguistic levels from lexical features to syntactic and semantic features.
Implementing speech quality and intelligibility assessment methods and related databases for Persian language
January 1, 2013 – Present
In this project we designed a software to measure the quality and intelligibility of noisy speech signals with both objective and subjective methods. Standard tests like DRT (Dynamic Rhyme Test) for intelligibility and MOS for quality are designed implemented and the required speech signals for Persian language were recorded.
Ontology development (commonsense knowledge ontology)
January 1, 2011 – Present
Putting human commonsense knowledge into computers has always been a long standing dream of artificial intelligence (AI). Since the first days of its appearance, AI knowledge engineers were studying hard to get round this bottleneck. The cost of several tens of millions of dollars and times have been covered so that the computers could know about “objects falling, not rising.”,” running is faster than walking" And “death is the end of the life”. The large database was built, automated and semi-automated methods were introduced and volunteers’ efforts were utilized to achieve this, but an automated, high-throughput and low-noise method for commonsense collection still remains as the holy grail of AI.
Speech enhancement module in wireless communication system
January 1, 2010 – Present
In this project we designed signal processing techniques to remove an specific non-stationary noise from noisy speech signals. We used Statistical and Spectral Subtraction methods to calculated the frequency content of the noise as basis but our techniques has novelties in noise estimation.
AWS Certified Machine Learning Engineer – Associate
Amazon Web Services (AWS)
June 24, 2026 – Present
AWS Certified AI Practitioner
Amazon Web Services (AWS)
June 24, 2026 – Present
AWS Certified Machine Learning – Specialty
Amazon Web Services (AWS)
June 24, 2026 – Present
Six Sigma Foundations
June 24, 2026 – Present
AWS Certified Solutions Architect - Associate (SAA-C02): 4 Compute Services
June 24, 2026 – Present
Certified SAFe® 4 Agilist
SAFe by Scaled Agile, Inc.
June 24, 2026 – Present
LUMA Institute Certified Practitioner of Human-Centered Design
LUMA Institute
June 24, 2026 – Present
Natural Language Processing (NLP) Microsoft Course
Microsoft
June 24, 2026 – Present
Cultural Fit Analysis
The candidate's diverse experience across academia, research, and industry, including major consulting firms, indicates adaptability and a broad perspective. Their involvement in co-founding a computational linguistics department and supervising graduate students suggests a proactive and innovative mindset. The range of projects, from ontology development to speech enhancement and text comprehension, shows a strong interest in fundamental AI problems and their practical applications. The target role of 'AI Engineer' aligns well with their deep technical background and leadership experience in AI/ML/NLP.
Soft Skills & Operational Fit
The candidate's extensive experience in leadership roles at consulting firms suggests strong communication, project management, and team empowerment skills. Their academic background as a lecturer and co-founder of a department indicates a capacity for mentorship and curriculum development. The project descriptions, while lacking specific technologies, demonstrate a problem-solving mindset and an ability to tackle complex AI challenges. The certifications in Six Sigma and SAFe Agilist suggest an understanding of operational excellence and agile methodologies.