Completed my M.S. in Computer Science at ASU
Graduated with a 4.0 GPA after earning top-5% marks in all 9 courses.
Irish Mehta | Machine Learning Engineer, Applied Scientist & Researcher
I'm Irish Mehta
A Machine Learning Engineer
ML engineer · applied scientist
Currently exploring
Production ML, applied AI, and research engineering
About me
I turn hard ML problems into systems people can use.
I’m a machine learning engineer and applied scientist who works from the first experiment to the final product.
Over the last 6 years, I’ve shipped production models for supply-chain and consumer products, then extended that foundation through research in computer vision, remote sensing, NLP, and generative AI. I enjoy the space between modeling and engineering: making a system accurate, scalable, observable, and useful to the person on the other side of it.
I recently completed my M.S. in Computer Science at Arizona State University, where I worked across Mars remote-sensing research, analytical systems, and efficient uncertainty estimation.
What keeps me curious
I have a soft spot for automation. I enjoy finding repetitive, frustrating parts of a process and turning them into tools that quietly give people time back—whether that means an ML service, a browser-based workflow, or a small script that removes a daily annoyance.
About Me
I'm a developer with three years of industry experience in building Machine Learning models. I have a solid background in end-to-end development, with a focus on building generalizable, scalable and efficient solutions.
I take pride in my ability to innovate and think out of the box. I am an expert user of Python and I am familiar with a wide range of tools and frameworks to solve any problem at hand. For me, tools are mediums to put creative throughts into actions. I am a strong advocate of open source and I feel that sharing knowledge is the best way to contribute to the community.
I recently completed my Master's in Computer Science at Arizona State University, where my goal was to upskill myself in all the subfields of AI. This included Classical ML, Generative AI, Natural Language Processing, Computer Vision, and Robotics. Parallel to academics, I was working as a Data Scientist and Researcher at different laboratories to apply learned theory to real-world problems. I am now looking for new opportunities to apply my skills and knowledge to solve challenging problems in the industry. I am open to roles in Machine Learning, Applied Science, Data Science, AI Research, and Software Development. Please feel free to reach out to me if you have any opportunities or if you just want to connect!
Experience & education
A track record of applied ML.
From production models to research systems, I build practical machine-learning solutions and measure what changes after they ship.
Aug 2024 – May 2026
M.S. Computer Science
Arizona State University
NLP •
Topics in Generative AI •
Cloud Computing •
Data Mining •
Semantic Web Mining •
AI in Robotics •
Advances in Robot Learning •
Data systems for ML •
Data Processing at Scale•
Knowledge Representation •
Topics in Generative AI •
Cloud Computing •
Data Mining •
Semantic Web Mining •
AI in Robotics •
Advances in Robot Learning •
Data systems for ML •
Data Processing at Scale•
Knowledge Representation •
- Top 5% in 9/9 courses taken
- 4.0 GPA
- Engineering Graduate Scholarship (rewarded to top 10% candidates).
- Worked at Kerner Lab in Partnership with NASA: Ran 20 k+ computer-vision experiments on Mars data using ASU HPC clusters (Slurm + Python) & developing foundational models based on orbital/rover data.
- Worked at Decision Theater @ ASU on analytical models and dashboards with partner organizations
- Worked at ACME lab @ ASU towards developing efficient solutions for uncertainty estimation in deep learning models
Oct 2023 – Jun 2024
Machine Learning Engineer
O9 SolutionsPython • Spark • Supply-Chain ML • Client Interaction
- Productionised hybrid clustering to optimise inventory for 10 Fortune 500 clients, cutting E2E runtime by 20%.
- Delivered ARIMA-based line-speed forecasts for AB InBev, raising accuracy by 10% & plant throughput 5%.
Oct 2022 – Oct 2023
Machine Learning Engineer
Head Digital Works MLOps • AWS • Micro-services • Real-time ML
- Won "Innovator of the Year" for card-arrangement optimisation; boosted engagement by 30%, cut fraud by 8%.
- Fast-tracked to MLE in 15 months; shipped 3 revenue-bearing features in year 1.
- Built MLService (AWS event-driven model deployment service), enabling ML-driven marketing campaigns, lifting daily active users by 10% and revenue by 0.25%.
Jun 2021 – Sep 2022
Associate Data Scientist
Head Digital WorksXGBoost • Fuzzy Logic • ETL
- Deployed a real-time retention model combining XGBoost & fuzzy logic, raising daily transactions 5%.
- Reshaped A23's loyalty program, segmenting 5.8M+ users through Python and advanced pivot table analytics to compute Lifetime Value (LTV) and balance cost-benefit allocation, enhancing customer retention by 15%
2017 – 2021
B.E. Electronics & Communication
BITS Pilani
Electronics • Signal & Image Processing
- Mentored 8 junior undergraduates across academic, co-curricular, and extracurricular pursuits, supporting their transition from school to college.
- Led a 40-member photography team, managing event coverage, inventory, and post-processing during the 2018-2019 academic year.
- Co-founded and led a 20-member acapella crew that won competitions across India and performed at campus events.
Projects
Showcasing some of my favorite work and personal projects.
SudoHint
React • JavaScript • CSS • GitHub Pages • Sudoku • OCR • Computer Vision • Responsive UI
A client-side Sudoku learning app that manages 81-cell game state in real time and supports browser-only image import with grid detection, perspective correction, cell segmentation, and OCR review. It also includes validation, timers, responsive mobile UI, and auto- or manual-candidate modes without a backend.
Multi-ATS Job Intelligence Platform
Python • LLMs • Qwen • Google Sheets API • GitHub Pages • Job Matching • Information Extraction • Data Pipelines
A multi-ATS job intelligence platform that ingests postings across 8.6K+ companies and 7 ATS platforms, filters 3.2K+ relevant ML and data roles, and publishes a searchable dashboard for daily job discovery using LLM-based relevance scoring and structured metadata extraction.
AutoFrame
Python • Vertex AI • Gemini • Computer Vision • VLMs • Android • ESP8266 • Automation • Human-in-the-Loop Systems
AutoFrame is an autonomous photography assistant that captures overview images, proposes better camera poses with Gemini reasoning, and iterates with Android and ESP8266 hardware support to optimize portrait framing.
MOMO
Python • Foundation Models • Remote Sensing • Computer Vision • Vision Transformers • Model Merging • Mars Science • Representation Learning
MOMO is a multi-sensor foundation model for Mars remote sensing that merges representations from HiRISE, CTX, and THEMIS and supports downstream tasks across a wide range of Martian orbital resolutions.
Mars-Bench
Python • PyTorch • Deep Learning • Computer Vision • Image Classification • Semantic Segmentation • Object Detection • Model Finetuning • Hugging Face • Benchmarking • Research
Mars-Bench brings 20 standardized datasets for classification, segmentation, and detection of Martian features like craters, cones, boulders, and frost, aiming to make evaluation consistent and to catalyze Mars‑specific foundation models.
Adaptive Learning Platform
Multi-Armed Bandit • Adaptive Learning • Reinforcement Learning • Machine Learning
An adaptive learning system driven by a Multi-Armed Bandit controller to optimize question difficulty in real-time. The architecture uses a Streamlit frontend, a Groq API-based question generator, and JSON for persistent progress saving.
DishCovery
Python • Google Gemini AI • Snowflake Cortex API • GCP • Next.js • TypeScript • Natural Language Processing • Vector Search • Full-Stack Development
DishCovery is a food discovery app that analyzes restaurant menus and enables natural language query-based filtering of dishes and restaurants.
Silkot Silicones
Next.js • React • TypeScript • SEO Optimization • Vercel • Web Development • Responsive Design • Frontend Development
Designed and developed a responsive website for Silkot Silicones, a leading manufacturer of silicone products. The website features a clean, modern design with a focus on usability and SEO.
CounterEcho AI
NetworkX • Graph Theory • Social Network Analysis • Counter-narratives • Louvain Algorithm • Symbolic Reasoning
A vulnerability-based strategic counter-narrative system that analyzes social network data to generate targeted counter-narratives based on user vulnerability scores.
Earnings Call RAG
Python • FastAPI • LangChain • RAG • FAISS • Vector Databases • Embeddings • Financial AI • Document Analysis • Retrieval-Augmented Generation
Earnings Call RAG Bot is a Retrieval-Augmented Generation system designed for analyzing and querying financial documents (Currently supports earnings call transcripts)
Candidate Recommendation System
Python • LLMs • NLP • Resume Ranking • BERT • Embeddings • Vector Search • ReRanking
AI-powered resume ranking system that intelligently matches candidates to job requirements.
Prompt Engineering
Python • Large Language Models • LLMs • Generative AI • Prompt Engineering • OpenAI • GPT • Claude • Prompt Optimization • AI Best Practices
Best‑practice prompts and tutorials for large language models.
Machine Learning from Scratch
Python • NumPy • PyTorch • Deep Learning • Neural Networks • Machine Learning Algorithms • Linear Algebra • Gradient Descent • Backpropagation
From-Scratch implementations of classic machine‑learning and deep learning algorithms.
Auto Storage Categorization
Python • Natural Language Processing • NLP • Text Classification • Google Drive API • Document Processing • Automation • File Organization • Text Analysis
NLP‑powered bot that auto‑sorts Google Drive files into logical folders.
Movie Recommendation System
Python • Scikit-learn • Machine Learning • Recommender Systems • Collaborative Filtering • Content-Based Filtering • Hybrid Recommenders • Data Science
Hybrid recommender merging content clues, ratings, and composite ranking.
Predictive Modelling for Composites
Python • SciPy • Statistical Modeling • Regression Analysis • Machine Learning • Optimization • Data Science • Predictive Analytics
Regression models to optimize composite manufacturing parameters.
AI Engineering
End-to-end AI products, multimodal systems, and intelligent interfaces.
Machine Learning
Models, benchmarks, and learning systems built around real technical problems.
Data Science
Analytics and predictive systems that turn data into useful decisions.
Signals of progress
What’s new.
A few recent milestones from my work across applied machine learning, research, and AI engineering.
AutoFrame received a best course project award
A software-assisted hardware system for automated photography.
MOMO accepted at CVPR 2026
A multisensor foundation model for Mars remote sensing.
Earlier milestones
2025Received the Best Poster Award at the NeurIPS ML for Physical Sciences Workshop.
Submitted a CVPR 2026 paper under the guidance of Dr. Hannah Kerner.
Built DishCovery, a food discovery app, at Sunhacks 2025.
Received acceptance for a NeurIPS paper in the Datasets and Benchmarks track.
Completed a summer internship at ASU’s ACME Lab focused on efficient uncertainty estimation in deep learning.
Submitted a NeurIPS 2025 paper in the Datasets and Benchmarks track.
Completed the Spring 2025 semester with a 4.22 GPA and top-5% marks in two courses.
Started working at Decision Theater at ASU.
Started volunteering at the Kerner Lab at ASU.
Contact
Let’s talk.
Interested in build something useful?
I’m open to machine-learning, applied AI, and research-engineering opportunities. If you’re building something interesting—or have a process that should be automated—send me a note.
ihmehta@asu.eduUsually quickest by email. I’ll get back to you as soon as I can.