Itrat Rahman
New York, NY H-1B | Open to roles in New York (onsite/hybrid) or Remote | Looking for H1B transfer until I receive EAD

Engineering
Intelligent Futures

I am a Senior AI Engineer/Data Scientist with over 9 years of technical experience with expertise in Computer Vision, NLP, LLM, GenAI, Chatbot, Agentic AI, MLOps, Cloud Technologies, Big Data Analytics, and Data Science. I have extensive AI/Data product development experience; transformed pet projects into startup & SAAS products; wore multiple hats and worked beyond corporate boundaries for product launches, attended client meetings to pitch products.
UCL MEng, First Class Honours (All 4 years)
Single Page Resume
+1 (832) 670-1119
rahmanitrat@gmail.com
github.com/itratrahman
linkedin.com/in/itratrahman

Professional Summary

  • 🚀 Senior AI Engineer/Data Scientist with over 9 years of technical experience with expertise in Computer Vision, NLP, LLM, GenAI, Chatbot, Agentic AI, MLOps, Cloud Technologies, Big Data Analytics, and Data Science
  • 💬 Natural Language Processing: Engineered production-grade NER and Bengali ASR models achieving 95% entity extraction accuracy and industry-leading low error rates (2.63 LD) for automated voice input
  • 🤖 GenAI & LLM & Agentic AI: Architected RAG-based agentic workflows and Custom GPTs for SharePoint/CRM that improved enterprise research and analytics productivity by 5x–10x through automated document parsing, NLQ interfaces etc.
  • 👁️ Computer Vision: Deployed scalable document & biometric verification and surveillance systems (99.5% accuracy), optimizing deep learning models for real-time inference on both servers and edge devices.
  • ⚙️ MLOps: Established automated CI/CD and continuous retraining pipelines using Airflow and Jenkins, ensuring seamless deployment, versioning, and monitoring of deep learning models with zero downtime.
  • 📊 Data Science: Delivered actionable business value through AutoML forecasting and A/B testing that drove a 7% increase in transaction volume, alongside geospatial optimizations that significantly reduced operational logistics costs.
  • 🔧 Data Engineering: Built high-throughput distributed pipelines processing 100M+ monthly records, accelerating data processing speeds by up to 30x.
  • ☁️ Cloud Computing: Designed serverless AI architectures on GCP and Azure, utilizing Cloud Run and Azure Functions to deploy scalable microservices and secure middleware for enterprise data integration.
  • 💡 Extensive AI/Data product development experience; transformed pet projects into startup & SAAS products; wore multiple hats and worked beyond corporate boundaries for product launches, attended client meetings to pitch products
9+ Years
End-to-End AI & Data Experience
100M+
Records Processed Monthly in Pipelines
99.5%
Face Recognition Accuracy (Custom Dataset)
5–10x
Boost in Enterprise Research Productivity

📖 Career Bio

Professional Journey

For over nine years, I have operated at the intersection of deep technical research and scalable product engineering, building a reputation as a Senior AI Engineer and Data Scientist who translates complex algorithms into tangible business return on investment. A First Class Honours graduate from University College London (UCL), I distinguish myself not just by building models, but by architecting end-to-end production systems that solve ambiguous business problems. My career is defined by a unique ability to transform experimental "pet projects" into fully commercialized SaaS products and startups, effectively bridging the gap between the research lab and the enterprise boardroom.

Most recently, I have been at the forefront of the Generative AI revolution, designing Agentic AI systems that fundamentally change how enterprises handle knowledge. At Ascend Performance Materials, I architected sophisticated RAG-based chatbots and Custom GPT agents that integrated seamlessly with internal data silos like SharePoint and Dynamics 365. These were not mere prototypes; they were robust operational tools that revolutionized internal workflows, accelerating literature reviews by five to ten times and doubling the efficiency of CRM analysis. Beyond the modeling layer, I demonstrated a keen awareness of enterprise security needs by engineering compliance pipelines that automated the centralization of ChatGPT workspace logs into SIEM systems, ensuring that innovation never came at the cost of auditability.

My capacity for product leadership was most visibly demonstrated during my tenure at SSL Wireless, Bangladesh's leading fintech provider. There, I led the development of a real-time face recognition system that achieved 99.5% accuracy on custom datasets. The success of this technology was so profound that it was spun off into a dedicated startup, SSL Sentinel, for which I secured the first eight enterprise clients. Simultaneously, I built DocChecker.ai, an automated eKYC and document verification SaaS. By processing complex OCR and resemblance checks with 98% mean average precision, this system reduced manual review backlogs by 40% and became a key revenue driver, proving my ability to deliver AI that directly impacts the bottom line.

Underpinning these product successes is a rigorous engineering mindset focused on scale and reliability. Unlike pure researchers who may stop at a notebook prototype, I am deeply embedded in the engineering lifecycle. I have architected multi-cloud data pipelines across GCP, AWS, and Azure capable of handling over 100 million rows per month, optimizing processing speeds by up to 30 times through distributed computing. My expertise spans the full MLOps stack, utilizing tools like Apache Airflow for orchestration, MLflow for tracking, and Docker with FastAPI for deploying containerized microservices that ensure zero downtime. Whether deploying pedestrian detection on edge devices like the Jetson Nano or forecasting transaction volumes that drove a 7% increase in gateway usage, I build systems designed to withstand the demands of real-world production environments.

Today, I offer a rare combination of skills—spanning Generative AI, Computer Vision, and Data Engineering—backed by a proven playbook for turning data into intelligent products. Currently based in the US on an H-1B visa, I am available for C2C contracts or full-time roles, ready to bring this blend of technical depth and strategic product vision to my next challenge.

9+ Years Experience Product & Startup Builder Full-Stack AI Engineer

🛠️ Technical Arsenal

GenAI, LLMs & Agentic AI

embeddings, semantic search, transformer model, LLM, Diffusion, RAG, vector database, GAN, GenAI, agents, chatbot, machine translation, speech recognition

Hugging Face OpenAI API / ChatGPT GPT action LangChain / LangGraph Llama / Llama index Vector DBs (Pinecone, Chroma, Weaviate)

Computer Vision & Imaging

Binary Morphology, Perspective/Affine Transformation, Edge Detection, OCR, object segmentation, object detection, facial recognition, video generation

TensorFlow / Keras PyTorch OpenCV Optuna / Keras Tuner

Data Science & Analytics

linear algebra, statistics, algorithms & data structures, data visualisation, A/B testing, classical machine learning, deep learning, SQL, churn Analysis, time series forecasting, natural language processing, named entity recognition

Python / SQL NumPy / pandas / SciPy Matplotlib / Seaborn / Bokeh / Folium Scikit-Learn / XGBoost / nltk Looker Studio / Tableau

Data Engineering & Big Data

Designing resilient, high-throughput data flows that feed analytics and ML.

  • ETL/ELT pipelines from MySQL, MongoDB & APIs into BigQuery.
  • Distributed processing with Spark/Dask & Dataflow.
  • Geospatial search and customer 360 profile pipelines.
BigQuery Apache Spark / Dask MySQL / PostgreSQL / MongoDB

MLOps, Cloud & Deployment

automating ML lifecycle using MLflow, orchestrating DataOps & MLOps pipeline using Airflow, automating deployment using CI/CD pipeline via Jenkins

GCP / Azure / AWS Docker / Docker-compose FastAPI / Flask Airflow / MLflow CI/CD (Jenkins) Git / GitHub / Linux / Bash / cron

Power Skills & Domains

English & Bengali Communication, PPT presentation, Client & Stakeholder Communication, Team Management, Project Management using Jira

Domain Knowledge: fintech, sms, telecom, facial image, pedestrian image, document OCR/parsing/inference, merchant onboarding, eKYC, crm, chemical industry, research & patent literature, robotics, embedded systems

English & Bengali PPT Presentation Client Communication Team Management Project Management (Jira)

💼 Professional Journey

Apr 2024 – Present

Senior Data Scientist

Astute 360 Corporation, Texas, USA

Astute 360 Corporation is a software consultancy company in Texas, USA that develops inhouse products and outsources consultants on C2C contracts. I worked as a GenAI Data Scientist at Ascend Performance Materials for 7 months.

  • Built and deployed a RAG-based resume-matching chatbot (GenAI, OpenAI) that retrieves best-fit resumes from user prompts and supports multi-turn conversation, improving resume discovery speed and relevance. Developed an LLM-based resume parser (GCP Vision API, LangChain) to extract and structure key resume fields with 99% extraction accuracy, storing documents and metadata in Pinecone for retrieval. Implemented LLM-assisted evaluation for chatbot answer quality, achieving 95% accuracy on internal evaluation.
  • Trained a bert model using TDSAE training scheme on the corpus of resume text to function as the embedder for the RAG vector search in the aforementioned chatbot.
  • Trained a multimodal model (Hugging face, NVIDIA GPU) on resume images + text using CLIP-style contrastive loss and LoRA/QLoRA fine-tuning to enable downstream tasks such as embedding-based clustering and ranking.
  • Clustered resume types via hierarchical clustering on multimodal embeddings (content, layout aesthetics, applicant features) to support segmentation and analytics.
  • Developed a resume ranking model using linear methods over multimodal inputs + LLM-inferred similarity + LLM-extracted QA metrics to improve candidate ordering.

Ascend Performance Materials, Texas, USA — Generative AI Data Scientist (C2C contract with Astute 360 Corp.)

Mar 2025 – Sep 2025

Ascend Performance Materials is the world's largest fully integrated producer of nylon 66 resin. I worked as a GenAI Data Scientist at Ascend on a C2C contract with my parent company, Astute 360 Corp with Collabera as the implementation partner. My role was to develop generative AI applications on top of enterprise data.

  • Shipped a SharePoint RAG agent using Custom GPT + GPT Actions with Azure Functions middleware (MS Graph search → targeted extraction → response to GPT), driving 5x–10x faster literature review for R&D stakeholders.
  • Delivered a Dynamics 365 CRM RAG agent where Custom GPT translates NL questions into OData queries; Azure Functions retrieves via Dataverse API, merges results, and runs QA using OpenAI + code interpreter, improving ad-hoc analytics productivity 1.5x–2x.
  • Built a spreadsheet-analysis Custom GPT that generates themed business reports with findings + recommendations, improving analyst throughput 5x–10x.
  • Created a SOP QA Custom GPT over multi-department safety procedures to improve on-the-spot access to protocols for high-risk plant operations.
  • Automated monthly journal + patent scouting using SERPAPI + semantic/LLM search, emailing top findings and reducing manual literature search workload by ~50%.
  • Designed a ChatGPT user-churn detection method from usage logs (drop-off in last 75 days + minimum monthly messages); used statistical analysis to set thresholds and automated churn reporting via email.
  • Built a secure pipeline to export ChatGPT workspace conversation logs to Rapid7 (daily + backfills, chunked payload delivery to webhook) to support compliance/audit archiving and enable GenAI adoption analytics.
Apr 2020 – Mar 2024

Senior Data Scientist

SSL Wireless, Dhaka, Bangladesh

SSL Wireless is the leading fintech and software corporation in Bangladesh, recognized as a top fintech company in the country. I was promoted to a senior role in January 2022 and since then led, managed, and mentored the next generation of data scientists.

  • Built an AI document verification system (Computer Vision, TensorFlow, PyTorch, OpenCV, NVIDIA GPU) for online merchant onboarding, improving onboarding efficiency 40%; trained document-scanning object detection models (~98% mAP) and resemblance checking binary classification models (~97% F1). Built OCR-based field extraction/validation with ~95% accuracy, reducing manual checks and improving onboarding quality. Productionized APIs with FastAPI + Docker and Jenkins CI/CD for reliable releases; automated end to end retraining/export of binary classification models with Airflow and implemented inference logging (MongoDB → BigQuery) powering near-real-time Looker Studio monitoring. Packaged the verification stack into a SaaS offering; supported customer pitching and onboarded 2 clients.
  • Built a real-time multi-face recognition platform (Computer Vision, TensorFlow, OpenCV, FastAPI, Docker, NVIDIA GPU) for camera streams; fine-tuned face recognition model for underrepresented faces and reached 99.5% accuracy on a custom test set. Deployed edge components (video streaming + face detection) on Jetson Nano and partnered with the engineering team to deliver real-time notifications plus a facial registration app (TensorFlow.js) that registers varied face poses. Supported creation of a dedicated sister company/startup (SSL Sentinel); contributed to sales pitching and onboarded 8 clients.
  • Implemented an intrusion detection system (Computer Vision, TensorFlow, OpenCV, FastAPI, Docker, NVIDIA GPU) that flags pedestrians crossing polygon boundaries; deployed a pedestrian detector on Jetson Nano and collaborated on a full backend/frontend monitoring panel.
  • Trained and deployed Bengali ASR (GenAI, LLM, Hugging Face, FastAPI, Docker, GCP Cloud Run) for merchant audio from CRM, improving accuracy with augmentation and achieving 2.63 Levenshtein distance on field-force speech.
  • Built and deployed Bengali NER (spaCy) to extract products/prices/quantities with 95%+ accuracy, enabling structured downstream CRM workflows.
  • Built and deployed blink-based liveness/spoof detection and integrated into Android via TensorFlow Lite + Kotlin, achieving >99% accuracy on video samples.
  • Delivered a face verification service (Tensorflow, FastAPI, Docker, AWS Lambda) to confirm whether two photos belong to the same person, reaching 97% accuracy on a large photo-pair corpus.
  • Automated route optimization by pulling merchant locations (Google Maps) and generating short-distance routes, saving ~$500–$700 per area plus hundreds of manual hours.
  • Reduced manual NID image review by ~400 hours across 200K images using CV quality checks + clustering/z-score threshold tuning.
  • Built AutoML forecasting for hourly/daily payment gateway volumes; automated retraining/forecasting with Vertex AI + BigQuery and validated campaign lift with A/B testing, contributing to ~7% usage increases during campaign seasons.
  • Implemented a fraud detection service (MySQL geospatial search + FastAPI) to flag merchant name/location manipulation and alert CRM apps in near real time.
  • Engineered a distributed pipeline processing 100M log rows/month (MySQL → unified profiles in MongoDB→ tokenized BigQuery tables via Dataflow), accelerating phases 6x–30x via Dask, concurrency, and indexing; automated runs with cron. The pipeline forms the bedrock of all the analytics work done at SSL wireless.
Jan 2017 – Mar 2020

Data Scientist

Learners & Yearners, Dhaka, Bangladesh

L&Y is a start-up that aims to develop friendly and animated online courses in AI and machine learning.

  • Designed and delivered an applied ML curriculum with code repositories, slides, and recorded lectures focused on low-level algorithm implementation.
  • Built a Python package implementing core ML algorithms from scratch (Decision Trees, KNN, K-means) and delivered small–medium EDA/predictive projects to reinforce concepts.
  • Taught a 2-month data science bootcamp to 10–15 learners, covering ML fundamentals and hands-on projects.
  • Contributed to startup business planning and pitching materials (business plan, pitch deck).
May 2016 – Aug 2016

Junior Data Scientist

Southeast Bank Limited, Dhaka, Bangladesh

Southeast Bank Limited is one of the mainstream local banks in Bangladesh.

  • Developed a fully-fledged optical character recognition software utilising advanced machine learning and computer vision techniques, to convert raw bank documents into text documents.
Nov 2015 – Jan 2016

Software Engineering Intern (AI/ML)

Insight Robotics Limited, Hong Kong

Insight Robotics is a Hong Kong Robotics company whose flagship product is a fire-detection robot.

  • Developed an image-based smoke detection algorithm utilising advanced machine learning and computer vision techniques, which halved the false alarm rate and improved the detection rate.

🚀 Selected Projects

Document Verification Application of EU Driving License Repo

A full-stack machine learning solution combining EfficientNet-B0 deep learning, PaddleOCR text extraction, and RetinaFace facial detection to authenticate EU Driving Licenses with 95% end-to-end accuracy and <2 second inference time.

Three-Stage Verification Pipeline:

  • Deep Learning Classification - EfficientNet-B0 binary classifier trained on 3,866 balanced images (3,000 licenses + 866 diverse documents across 11 categories). Achieves 97% validation F1 score using inverse frequency class weighting and transfer learning from ImageNet.
  • OCR Marker Validation - PaddleOCR engine extracts and validates all 11 required EU license fields (1, 2, 3, 4a-4d, 5, 7, 8, 9) through normalized text matching and regex patterns to ensure format compliance.
  • Facial Recognition Check - RetinaFace detector validates exactly one face is present with appropriate size constraints (2-60% relative area), preventing false positives from multi-face or non-photo documents.

Production Engineering Highlights:

  • ✅ MLOps Integration: Bayesian hyperparameter optimization with Optuna (50 trials), MLflow experiment tracking, automated model versioning with F1-score based selection
  • ✅ Scalable Architecture: FastAPI REST API with async MongoDB logging, thread-safe inference, health checks, hot-reload model capability
  • ✅ DevOps Ready: Docker + docker-compose deployment, comprehensive pytest suite, multi-stage container builds, non-root security
  • ✅ Performance: GPU/CPU auto-detection, <2s per image, 8MB size limits, proper memory management
  • ✅ Observability: 14 MongoDB indexes, comprehensive audit trails, aggregated performance metrics, real-time monitoring

Stock Market Agent Repo

An AI-powered stock market analysis system combining NeuralProphet time series forecasting, NewsAPI sentiment analysis, and LangGraph agentic workflows to generate automated investment recommendations for top tech stocks with 30-day price predictions and confidence intervals.

Five-Stage Analysis Pipeline:

  • 📈 Stock Data Ingestion - Automated fetching of 5-year daily OHLCV data from Financial Modeling Prep API for AAPL, MSFT, NVDA, AMZN, and GOOGL. Outputs NeuralProphet-ready CSV format (ds, y columns).
  • 🤖 Time Series Forecasting - NeuralProphet models trained per stock with 100 epochs, 10 changepoints, and yearly seasonality. Leverages PyTorch backend with automatic learning rate optimization.
  • 📊 Forecast Generation - 30-day price predictions with upper/lower 95% confidence bounds. Date-stamped CSV outputs enable historical tracking.
  • 📰 News Intelligence - NewsAPI integration fetching 100+ articles per company with newspaper4k full-text extraction. Enriched JSON with source, author, images, and complete article text.
  • 🧠 Agentic Decision Engine - LangGraph state machine with Ollama/Llama 3.2 LLM analyzes forecasts (<300 char summaries) and news (<1000 char summaries) to output INVEST/AVOID/NEUTRAL recommendations with reasoning.

Engineering Highlights:

  • ✅ Agentic Architecture: LangGraph StateGraph with conditional branching, LangChain prompt templates, local LLM inference via Ollama
  • ✅ Modular Pipeline: 5 independent scripts with clear data contracts (CSV → Model → Forecast → News → Report)
  • ✅ Production Ready: Argument parsing, credential management, error handling, organized output directories
  • ✅ Reproducibility: TensorBoard logging per stock, model versioning, timestamped outputs
  • ✅ Extensible: Easy to add new stocks, adjust forecast periods, or swap LLM backends

Codebase of ML Training Program Repo

Comprehensive hands-on curriculum combining mathematical foundations with practical ML implementations. From-scratch algorithm development using custom educational library (m_learn). 7 progressive modules with 40+ Jupyter notebooks covering core data science concepts.

Technical Stack:

  • Python 3.7+ with NumPy, Pandas, Matplotlib, Seaborn
  • Jupyter notebooks for interactive learning
  • Custom NumPy-based ML library (no external ML frameworks)

Key Modules:

  • 🧱 Foundations: Python, NumPy, Pandas, data visualization essentials
  • 📈 Regression: Linear, Ridge, Lasso with bias-variance analysis and regularization
  • 🏷️ Classification: Logistic regression, binary/multiclass, mini-batch gradient descent
  • 🔍 Non-Parametric: KNN, kernel regression, bandwidth optimization
  • 🎲 Probabilistic Models: Gaussian Naive Bayes, spam detection, sentiment analysis
  • 🌳 Tree Methods: CART algorithm, hyperparameter tuning, decision boundaries
  • 📉 Dimensionality Reduction: PCA with eigendecomposition, feature compression

Implementation Highlights:

  • Built 15+ ML algorithms from scratch without scikit-learn
  • Custom implementations: regression models, classifiers, clustering, PCA
  • Utility modules: data preprocessing, cross-validation, performance metrics
  • Real-world datasets: housing prices, insurance risk prediction

Learning Outcomes:

  • Deep understanding of algorithm mathematics and inner workings
  • Model evaluation: train-test splits, cross-validation, ROC/precision-recall curves
  • Hyperparameter optimization and regularization techniques
  • End-to-end ML pipelines from data exploration to deployment-ready models

Impact:

  • ✅ Educational resource bridging theory and practice
  • ✅ Self-contained modules for independent study or sequential curriculum
  • ✅ Production-quality code with comprehensive documentation

🎓 Education

UCL Logo
Sep 2011 – Jun 2015

Master of Engineering (Integrated 4-year Master's)

Course: Electronic and Electrical Engineering

University College London (UCL), London, UK

University College London (UCL) is a world-leading, multidisciplinary powerhouse ranked in the global top 10. As London's leading multidisciplinary university, we foster disruptive thinking, innovation, and global impact, producing top-tier, research-driven graduates ready to tackle complex challenges. Hire UCL talent for diverse, high-caliber, and critical-thinking expertise.

UCL Campus

Achievements & Awards:

  • Grade: First Class Honours
  • Obtained first class results in all 4 years
  • First class result in final year project
  • 2014 UROS Studentship (an award given to top undergraduates from UCL's Engineering Faculty)

Research Experience:

Final Year Project — KORUZA – Free Space Optical System

The project was sponsored by Koruza. The aim of project Koruza is to develop a low-cost open-source free space optical system for primary use in community wireless networks.

  • Developed a fog detection system based on image-based visibility estimation using a single webcam.
  • Developed novel error removing code, used to remove burst and random errors in the raw data collected from various sensors in KORUZA FSO unit.
  • Analyzed and assessed the processed data sets acquired from FSO units, and presented the data in various visualization forms.
  • Evaluated a strong anti-correlation between temperature change and output power of SFP module, of opposing FSO units.

Summer Research Experience — UROS Studentship, UCL Engineering Faculty

Undergraduate Research Opportunities Scheme (UROS) is a studentship awarded to the best undergraduates by UCL Engineering Faculty in order to encourage a career in Research and Development.

  • Researched and tested a new fibre-dispersion tolerant multi-subcarrier modulation scheme. Modeled & simulated cutting-edge optical transmitter and receiver of the modulation scheme.
  • Gained working knowledge of R&D and simulation.

Degree Certificate

Academic Transcript

Recommendation – Prof. Robert Killey

Recommendation – Dr. Edward Romans

📜 Certification

Coursera

Verified Courses

Deep Learning Specialization (5 courses)

In this specialization students learn to:

  • Build and train deep neural networks, identify key architecture parameters, implement vectorized neural networks and deep learning to applications
  • Train test sets, analyze variance for DL applications, use standard techniques and optimization algorithms, and build neural networks in TensorFlow
  • Build a CNN and apply it to detection and recognition tasks, use neural style transfer to generate art, and apply algorithms to image and video data
  • Build and train RNNs, work with NLP and Word Embeddings, and use HuggingFace tokenizers and transformer models to perform NER and Question Answering

Machine Learning Specialization (4 courses)

In this specialization students learn to:

  • Attain expertise in Machine Learning through rigorous coursework from leading researchers at the University of Washington
  • Gain applied experience in Prediction, Classification, Clustering, and Information Retrieval through practical case studies
  • Analyze large and complex datasets and create systems that adapt and improve over time
  • Build intelligent applications that can make predictions from data

Data Engineering, Big Data, and Machine Learning on GCP (5 courses)

In this specialization students learn to:

  • Design and build data processing systems on Google Cloud Platform
  • Extracting, Loading, Transforming, cleaning, and validating data
  • Enable instant insights from streaming data

Python Specialization (5 courses)

In this specialization students learn to:

  • Master fundamental programming concepts using the Python programming language
  • Work with data structures, networked application program interfaces, and databases
  • Design and create applications for data retrieval, processing, and visualization in the Capstone Project

⭐ Endorsements

"His contributions were pivotal in delivering key projects that significantly influenced new product development in the company."

Shahzada Redwan
Former CTO, SSL Wireless

"He possesses deep knowledge in Machine Learning, Computer Vision, NLP, and Generative AI, and excels at translating complex challenges into scalable, data-driven solutions."

Ketan Nirpagare
Data Scientist, Ascend Performance Materials

"Within two months he created a novel image-based smoke detection algorithm that the company implemented in its wildfire detection robots long term."

Tam Wai Ming
Former R&D Manager, Insight Robotics

"He consistently impressed me with his diverse skillset—proficient in Computer Vision, Natural Language Processing, and Big Data—with clear communication and a collaborative spirit."

Asruf-Ul Jubair
Former Principal Product Manager, SSL Wireless

"He is a standout Data Scientist who combines deep technical expertise with the soft skills needed to turn AI systems into commercial successes."

Musarrat Rahman
Former Senior Data Scientist, SSL Wireless

"Itrat is an outstanding student who graduated with a first class MEng, showing consistently high academic performance and strong experimental and analytical skills."

Dr Edward Romans
Associate Professor, UCL EE Department & LCN

"His academic ability is outstanding; he achieved yearly exam averages at First Class level in every year of the programme."

Prof Robert Killey
Professor, UCL EE Department

"He excels in finding new ideas to grow the generative AI space within a company and quickly assimilates into fast-paced work cultures."

Arnet Tuazon
Project Manager, Ascend Performance Material

"His code bases were clear, effective, readable, and reproducible, and his leadership and mentorship enabled me to progress rapidly in my career."

Khadija Akter Lima
Former Data Scientist, SSL Wireless

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