Rijul
Saini.

Ph.D. · Senior AI/ML Scientist & Engineer

Building production-grade machine learning systems that translate ambiguous business challenges into measurable outcomes — from LLM-powered RAG pipelines and agentic workflows to enterprise-scale time-series forecasting.

5+
Years in Industry (AI/ML)
10+
IEEE / ACM Publications
350+
Citations
4.0
Ph.D. GPA — McGill University
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Rijul Saini
Ottawa, ON, Canada
Ph.D., McGill University · GPA 4.0
Silver Medalist, ACM SRC 2022
Google Summer of Code × 2

About Me

Turning Data into
Decisions at Scale

I am currently a Senior AI Scientist at NAV CANADA, where I architect enterprise-grade ML systems that directly impact business-critical aviation KPIs. My work spans building hybrid deterministic + agentic RAG pipelines, automated data infrastructure, and executive-facing forecast-explanation platforms on Azure and Databricks.

I hold a Ph.D. in Applied Machine Learning from McGill University (GPA 4.0/4.0), where I was mentored by Professor Gunter Mussbacher. My doctoral research developed AI-powered recommendation systems for domain modelling, using NLP and knowledge graphs to make software requirements traceable and queryable.

Before NAV CANADA, I spent over three years at Bombardier Aerospace as a Senior Data Scientist — productionizing ML recommendation engines generating $100K+ in weekly revenue, building predictive maintenance pipelines for aircraft sensor data, and earning two company-wide Recognition & Mobilization awards. Prior to academia, I worked as a Software Analyst at Accenture (2013–2017).

I am a published IEEE researcher, a two-time Google Summer of Code contributor, and a committed technical mentor. My work is guided by a belief that great ML is not just accurate — it is explainable, reproducible, and trustworthy.

Technical Stack

Core Competencies

AI / ML
LLMsAI AgentsRAG Prompt EngineeringFine-Tuning TransformersKnowledge Distillation QuantizationBayesian Methods Model ExplainabilityGenerative AI
NLP / Deep Learning
HuggingFaceSpaCy PyTorchScikit-Learn Sentence-BERTVector Databases Knowledge GraphsNER NLTKEmbeddings
MLOps & Cloud
AWS SageMakerAzure DatabricksMLflow DockerKubernetes KedroCI/CD SnowflakeAzure Data Factory
Engineering
PythonSQL JavaSpark Pandas / NumPyREST APIs DjangoFlask React JSSpring Boot Microservices
Data & Analytics
Time-Series ForecastingPredictive Analytics Recommendation SystemsA/B Testing Statistical ModelingData Pipelines Anomaly Detection
Certifications & Recognition
AWS Solutions Architect – Associate Snowflake Data Warehousing ACM Silver Medalist Google Summer of Code × 2 McGill Doctoral Award

Career

Work Experience

Senior AI Scientist
NAV CANADA
Jul 2024 – Present Ottawa, ON
Architecting and leading development of an enterprise-grade time-series forecasting platform on Azure + Databricks, directly impacting business-critical aviation KPIs while reducing forecast latency by 60% through automation.
Engineered automated data ingestion and transformation pipelines using Azure Data Factory and custom Python orchestration, compressing a 1-week manual workflow to under 2 hours — a 96% efficiency gain.
Engineering an executive-facing variance explanation platform using Model-Driven Architecture, statistical decomposition, MLflow, knowledge graphs, and a hybrid deterministic + AI Agentic/RAG pipeline for full reproducibility and actionable business narratives.
Mentoring and upskilling a cross-functional team of junior data scientists, establishing best practices in code quality, testing, and model evaluation — increasing team velocity by 40%.
Azure DatabricksLLMs / RAGAI AgentsMLflowKnowledge GraphsTime-Series
Senior Data Scientist
Bombardier Aerospace
Mar 2023 – Jul 2024 Montréal, QC
Productionized an ML-powered recommendation engine for aircraft parts procurement using AWS SageMaker and custom ranking models — generating $100K+ in weekly incremental sales at scale.
Architected and deployed DataProbe, an end-to-end automated data analysis tool on AWS, reducing manual processing effort by 75% through automated statistical profiling and anomaly detection.
Built robust ML pipelines for predictive maintenance, processing multivariate time-series sensor data from aircraft systems to forecast component failures with ~90% precision, directly reducing unplanned downtime.
Collaborated with Platform Architects and DevOps to integrate models into a containerized microservices architecture (Docker + Kubernetes), enabling zero-downtime deployments and horizontal scaling.
AWS SageMakerDocker / K8sPredictive MaintenanceRecommendation Systems
RMP Award — DataProbe RMP Award — Production Impact AWS Solutions Architect Snowflake Certified
Student Intern — Deep Learning Research
National Research Council Canada
Sep 2022 – Dec 2022 Ottawa, ON
Conducted research applying deep learning to defect localization; developed YOLO and Faster R-CNN models in PyTorch for vehicle parts classification (mAP > 0.90).
PyTorchYOLOComputer Vision
AI Research Engineer (Intern)
Bombardier Aerospace
Jan 2021 – Sep 2022 Montréal, QC
Pioneered a predictive aerodynamics pipeline using Scikit-Learn to process 3D CFD data and predict aerocoefficients with >95% accuracy, reducing simulation cycle time by ~40%.
Developed metaheuristic optimization algorithms to automate conceptual aircraft design exploration, enabling evaluation of 10× more design configurations per sprint.
Designed microservices architecture with Django REST backend and React frontend as an interactive engineering workbench; deployed to internal cloud with CI/CD automation.
Scikit-LearnDjango / ReactCFD / AerodynamicsCI/CD
2nd Place — Leaders of Tomorrow 2021
Teaching Assistant
McGill University
2018 – 2021 Montréal, QC
Software Language Engineering (ECSE 439) — Winter 2021
Introduction to Software Engineering (ECSE 321) — Fall 2020, Fall 2019, Winter 2019, Fall 2018
Software Requirements Engineering (ECSE 326) — Fall 2020, Fall 2019, Winter 2018
Model-Based Programming (ECSE 223) — Fall 2019, Winter 2019
Student Developer — Google Summer of Code
Eclipse / Red Hat (via GSoC)
Summer 2019 & 2020
Collaborated with Red Hat to bring co-editing support to Eclipse Che 7.
Built a VS Code extension enabling conflict-free collaborative development and modelling.
Eclipse CheVS Code ExtensionOpen Source
Software Analyst
Accenture
Dec 2013 – May 2017 New Delhi, India
Delivered full-stack ABAP solutions for Portal and Finance clients, reducing support ticket volume by 67% through automated reporting interfaces.
Received the Outperform Award for innovative issue analysis and deploying production-stable fixes ahead of SLA.
ABAPSAPEnterprise Software

Research

Selected Publications

IEEE · ACM · Springer — peer-reviewed contributions in AI, ML, and model-driven engineering.

2025
ForeSPECT: A Model-Driven Framework for Validation and Traceability in Forecasting Systems
Saini, R. et al.
IEEE/ACM ASE Workshops, 2025
New
2022
Machine Learning-Based Incremental Learning in Interactive Domain Modelling
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
ACM/IEEE MODELS '22, Montreal, Canada
Acceptance Rate: 27%
DOI: 10.1145/3550355.3552421
2022
Automated, Traceable, and Interactive Domain Modelling
Saini, R.
ACM/IEEE MODELS '22 Companion, Montreal, Canada
🥈 ACM Silver Medalist
DOI: 10.1145/3550356.3552372
2021
Automated, Interactive, and Traceable Domain Modelling Empowered by Artificial Intelligence
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
Software & Systems Modeling (SoSyM), Springer
DOI: 10.1007/s10270-021-00942-6
2021
Automated Traceability for Domain Modelling Decisions Empowered by Artificial Intelligence
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
29th IEEE International Requirements Engineering Conference (RE 2021), IEEE CS
Acceptance Rate: 29%
DOI: 10.1109/RE51729.2021.00023
2021
A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems
Combemale, B., Kienzle, J., Mussbacher, G., ..., Saini, R., et al.
IEEE Software, 38(4):71–84
DOI: 10.1109/MS.2020.2995125
2021
DoMoBOT: An AI-Empowered Bot for Automated and Interactive Domain Modelling
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
ACM/IEEE MODELS 2021, Fukuoka, Japan
DOI: 10.1109/MODELS-C53483.2021.00090
2021
Towards Conflict-Free Collaborative Modelling using VS Code Extensions
Saini, R. and Mussbacher, G.
HoWCoM 2021, ACM/IEEE MODELS-C, Fukuoka, Japan
DOI: 10.1109/MODELS-C53483.2021.00013
2020
Towards Queryable and Traceable Domain Models
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
IEEE RE 2020, RE@NEXT! Track, Zurich, Switzerland
Acceptance Rate: 31%
DOI: 10.1109/RE48521.2020.00044
2020
DoMoBOT: A Bot for Automated and Interactive Domain Modelling
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
MDE Intelligence 2020, Montreal, Canada, ACM
DOI: 10.1145/3417990.3421385
2020
Artificial Intelligence Empowered Domain Modelling Bot
Saini, R.
MODELS 2020, Montreal, Canada, ACM
DOI: 10.1145/3417990.3419486
2020
A Neural Network Based Approach to Domain Modelling Relationships and Patterns Recognition
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
MoDRE 2020, Zurich, Switzerland, IEEE CS
DOI: 10.1109/MoDRE51215.2020.00016
2019
Teaching Modelling Literacy: An Artificial Intelligence Approach
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
Educators Symposium, MODELS 2019, Munich, IEEE CS
DOI: 10.1109/MODELS-C.2019.00108
2019
Towards Web Collaborative Modelling for the URN Using Eclipse Che and Theia IDE
Saini, R., Bali, S., and Mussbacher, G.
MiSE 2019, Montreal, IEEE CS
DOI: 10.1109/MiSE.2019.00010
2025
ForeSPECT: A Model-Driven Framework for Validation and Traceability in Forecasting Systems
Saini, R. et al.
IEEE/ACM ASE Workshops, 2025
New · 2025
2022
Machine Learning-Based Incremental Learning in Interactive Domain Modelling
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
ACM/IEEE MODELS '22, Montreal, Canada
Acceptance Rate: 27%
DOI: 10.1145/3550355.3552421
2022
Automated, Traceable, and Interactive Domain Modelling
Saini, R.
ACM/IEEE MODELS '22 Companion, Montreal
🥈 ACM Silver Medalist
DOI: 10.1145/3550356.3552372
2021
Automated, Interactive, and Traceable Domain Modelling Empowered by AI
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
Software & Systems Modeling (SoSyM), Springer
DOI: 10.1007/s10270-021-00942-6
2021
Automated Traceability for Domain Modelling Decisions Empowered by AI
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
IEEE RE 2021, Notre Dame, USA
Acceptance Rate: 29%
DOI: 10.1109/RE51729.2021.00023
2021
A Hitchhiker's Guide to Model-Driven Engineering for Data-Centric Systems
Combemale, B., Kienzle, J., Mussbacher, G., ..., Saini, R., et al.
IEEE Software, 38(4):71–84
DOI: 10.1109/MS.2020.2995125
2021
DoMoBOT: An AI-Empowered Bot for Automated and Interactive Domain Modelling
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
ACM/IEEE MODELS 2021, Fukuoka, Japan
DOI: 10.1109/MODELS-C53483.2021.00090
2021
Towards Conflict-Free Collaborative Modelling using VS Code Extensions
Saini, R. and Mussbacher, G.
HoWCoM 2021, Fukuoka, Japan, ACM
DOI: 10.1109/MODELS-C53483.2021.00013
2020
Towards Queryable and Traceable Domain Models
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
IEEE RE 2020, RE@NEXT! Track, Zurich
Acceptance Rate: 31%
DOI: 10.1109/RE48521.2020.00044
2020
DoMoBOT: A Bot for Automated and Interactive Domain Modelling
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
MDE Intelligence 2020, Montreal, ACM
DOI: 10.1145/3417990.3421385
2020
Artificial Intelligence Empowered Domain Modelling Bot
Saini, R.
MODELS 2020, Montreal, ACM
DOI: 10.1145/3417990.3419486
2020
A Neural Network Based Approach to Domain Modelling Relationships and Patterns Recognition
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
MoDRE 2020, Zurich, IEEE CS
DOI: 10.1109/MoDRE51215.2020.00016
2019
Teaching Modelling Literacy: An Artificial Intelligence Approach
Saini, R., Mussbacher, G., Guo, J.L.C., and Kienzle, J.
Educators Symposium, MODELS 2019, Munich, IEEE CS
DOI: 10.1109/MODELS-C.2019.00108
2019
Towards Web Collaborative Modelling for the URN Using Eclipse Che and Theia IDE
Saini, R., Bali, S., and Mussbacher, G.
MiSE 2019, Montreal, IEEE CS
DOI: 10.1109/MiSE.2019.00010
2019
Modelling Bot — A Modelling Buddy
Saini, R., Mussbacher, G., Guo, J., and Kienzle, J.
MiSE 2019, Montreal, Canada (Presentation)

Updates

Latest News

ForeSPECT paper accepted at IEEE/ACM ASE Workshops 2025.
Joined NAV CANADA as Senior AI Scientist, building enterprise forecasting + RAG systems on Azure — Jul 2024.
Invited Reviewer for the Science of Computer Programming Journal, February 2023.
Received the Silver Medal 🥈 in the ACM Student Research Competition — October 2022.
Research paper selected for presentation at MODELS'2022, Montreal.

Service

Community Service

Program Committee Member — MoDRE Workshop at RE'26.
Invited Reviewer — Science of Computer Programming Journal, 2023.
General Co-Chair — 12th International MoDRE Workshop at RE'22.
Program Committee Member — Tools & Demonstrations track, MODELS'22.
Program Committee Member — MDE Intelligence Workshop at MODELS'21.

Work

Demos & Projects

Data Science Challenger
Data Science Challenger
Interactive data science challenge platform.
 Knowledge Graph Traceability
Traceability via Knowledge Graphs
Automated traceability for domain modelling decisions.
 Collaborative Modelling
Collaborative Modelling
VS Code extension for conflict-free collaborative development.
 DoMoBOT Traceable
DoMoBOT — Traceable Modelling
Demo: automated and traceable domain modelling bot.
 DoMoBOT Interactive
DoMoBOT — Interactive Modelling
Demo: AI-empowered interactive domain modelling.
 GSoC 2020
GSoC 2020 — VS Code Atom Teletype
Co-editing support in Eclipse Che via VS Code extension (Part II).
 GSoC 2019
GSoC 2019 — VS Code Atom Teletype
Collaborative development tooling for Eclipse Che (Part I).
 Eclipse Che on Minikube
Running Eclipse Che on Minikube
Guide for local cloud IDE deployment on Minikube.

Say Hello

Get in Touch

Whether you want to discuss an exciting AI/ML opportunity, collaborate on research, or just connect — I'd love to hear from you. Feel free to reach out through any channel below.

rijulsaini.mait@gmail.com linkedin.com/in/rijul-saini