AI Research Engineer · Toronto, Ontario, Canada. AI Research Engineer focused on reliable agentic systems, agent evaluation, and auditable AI workflows.
AI Research Engineer with a Ph.D. in Computer Science and 3+ years of experience building and evaluating LLM agents, deep-learning pipelines, and search-based AI systems across industry, government, and academia. Builds agent harnesses, retrieval and tool-use workflows, evaluation infrastructure, and reproducible ML prototypes; published at AAAI, at Canadian AI, and in peer-reviewed journals.
Open to research collaborations and selective advisory work in agent evaluation and reliability.
Experience
AI Engineer, Agentic Systems
Flybits · Full-time · Toronto
Agentic systems that plan, call tools across internal and partner APIs, and complete multi-step tasks with every decision logged for end-to-end auditability.
Retrieval over structured user-context graphs, pairing learned models with symbolic constraints so responses stay grounded in real customer state rather than model priors.
The evaluation loop — task completion, groundedness, faithfulness, recovery from tool failure — and using it to tune model routing across capability, latency, and cost.
AI Research Engineer, sAIpien
MIT Media Lab · Part-time research affiliation
Perspective-aware agents, privacy-preserving long-term memory, and benchmarks for transparency and human oversight.
Prototyping perspective-aware agents and privacy-preserving long-term memory through adaptive knowledge graphs (Chronicles).
HCI² benchmarks for transparency and human oversight.
Postdoctoral Fellow / AI Research Engineer
Toronto Metropolitan University · Part-time academic appointment · Toronto
Reproducible evaluation harnesses for LLM-agent reasoning, orchestration, context management, and reliability.
Evaluation harnesses in Python and PyTorch — trajectory logging, counterfactual replay, automated scoring.
Taking research questions to working prototypes with experimental protocols, metrics, and publications, shipping code other researchers can run and reproduce.
Machine Learning Engineer (Graduate Research)
Toronto Metropolitan University · Toronto
AI prototypes spanning multi-agent systems, heuristic search and planning, deep learning, reinforcement learning, and GAN-based synthetic-data generation.
Training and inference pipelines with PyTorch, Hugging Face, scikit-learn, and XGBoost; experiments containerized with Docker and checked through GitHub Actions CI.
Controlled experiments with ablations and failure-mode profiling, published at AAAI, Canadian AI, and in peer-reviewed journals.
ML Engineer / Research Scientist Intern
National Research Council Canada · Toronto
Knowledge-informed machine-learning models for anomaly detection over large-scale, severely imbalanced telemetry data.
Domain-derived features that reduced dependence on large labelled datasets.
Reproducible, peer-reviewed experiments across an interdisciplinary team.
An agent's written reasoning is not evidence that the reasoning caused the answer.
23 of 30 — audited trajectories showed a faithfulness violation under the counterfactual-intervention protocol. This is an audit finding about the agents under test, not a failure rate of the tool. 23/30 is 76.7%.
A single LLM asked to build software stops at the first thing it gets wrong.
Self-repair — execution feedback drives automatic repair — demonstrated end to end, not benchmarked. No benchmark, baseline, or success rate is reported in the paper, so no comparative claim is made here.
When a heuristic gives a search no gradient to follow, the search stalls in a region it cannot see its way out of.
70.3% — of unbounded-region tasks, where the restarting-random-walk planner was faster than or uniquely successful against breadth-first search. The 70.3% is measured on unbounded-region tasks specifically, not on planning benchmarks as a whole. "Uniquely successful" means the comparator did not solve the task at all.
A 210-image, severely imbalanced dataset is too small to train a classifier that generalizes.
91.5% — downstream classification accuracy — 4 points over duplication-based oversampling, 5.5 over none. This is downstream classifier accuracy on the microplastics dataset, not a measure of the generated images themselves.
Technical skills
Languages
Python · C/C++ · TypeScript · Java
Machine learning
PyTorch · Hugging Face · scikit-learn · XGBoost · Deep learning · Reinforcement learning · Fine-tuning · GANs · Synthetic data
LLM & agent systems
LLM agents · Tool and function calling · Multi-agent orchestration · Retrieval-augmented generation · Context and memory management
Agentic AI workflows for reliable automated reasoning — LLM-agent orchestration, reasoning loops, tool use, and evaluation of agent faithfulness. GPA: A+. Coursework: Heuristic Search, Deep Learning, Directed Intelligent Robotic Systems.
Bachelor of Computer Science
Toronto Metropolitan University
A+ in every AI course: Machine Learning, Artificial Intelligence, Reinforcement Learning, and Computer Vision.
Publications
The complete record — peer-reviewed papers and preprints, labelled separately, with citations — is on the research page. The Google Scholar profile is authoritative for citation counts.