Experience

Roles

1. May 2025 – Present

   Hammerspace

   Burnaby, BC (Remote)

   Software Engineer, Advanced Research Engineering

   • Built grounding and evaluation infrastructure to make LLM output verifiable — deterministic checks and LLM-judge validation running as CI merge gates, a scheduled audit job opening its own remediation pull requests, and server-side access-control filtering with human review gating AI-authored content.
   • Designed and built an internal knowledge platform end-to-end (Python/FastAPI, Next.js/TypeScript) — ingestion pipelines across multiple document and collaboration sources, an LLM synthesis workflow where every published claim traces back to an immutable source at fragment granularity, and a review, search & graph-exploration web app, front end included; demoed the platform to leadership; primary engineer across 300+ipull requests as primary engineer on the knowledge platformConfirmed by Paige on 2026-10-06; updated from the resume of 2026-09-11.Source: Facts confirmed by Paige, 2026-09-08 to 2026-10-06 pull requests.
   • Built the retrieval and conversational layer — an MCP server under access-control filters, BM25F search with query expansion, an offline retrieval evaluation harness over golden queries, and a streaming (SSE) chat service with an agent loop and citation guardrails on model output.
   • Contributed to a multi-agent infrastructure-automation platform on the OpenAI Agents SDK (containerized agents, LLM-based routing between specialized agents, MCP tool integrations, guardrails and permissioning) and wrote concurrent LLM API workloads at volume; fine-tuned a 7Biparameters in the fine-tuned code-generation model (CodeLlama-7B-Instruct, LoRA)Source: Resume, revised 2026-09-11 code-generation model (LoRA, CodeLlama-7B-Instruct, in PyTorch with Hugging Face PEFT), building its instruction dataset and a ~95iqueries in the fine-tuned model's evaluation suiteSource: Resume, revised 2026-09-11-query evaluation suite with accuracy and latency logging, containerized for remote GPU inference.

   • Python
   • FastAPI
   • TypeScript
   • Next.js / React
   • MCP
   • Agent loops & tool calling
   • RAG
   • BM25F search & query expansion
   • LLM evaluation harnesses
   • CI-gated validation
   • Grounding & claim verification
   • Access control & permissions
   • LoRA fine-tuning
   • PyTorch
   • Docker & containerization
   • OpenAI Agents SDK

2. Sept 2024 – Feb 2025

   Microsoft

   Redmond, WA, USA

   Software Engineer, M365 Core Substrate SHIELD Security

   • Engineered a security-focused anomaly detection & threat intelligence system using sequential log data, applying temporal pattern analysis to proactively detect & mitigate emerging cyber threats.
   • Partnered with security & infrastructure teams to design and deploy automated, scalable models across live telemetry, translating asynchronous sequence data into actionable risk profiles; triaged and mitigated live security incidents on production services.

   • Anomaly detection
   • Time-series analysis
   • MLOps

3. Nov 2022 – Sept 2024

   Microsoft

   Redmond, WA, USA

   Software Engineer, Azure Cloud Storage Performance Infrastructure & Analytics

   • Designed and developed AI-powered system diagnostics, optimizing large-scale, cloud-based retrieval and anomaly detection models for distributed AI systems.
   • Built telemetry analytics pipelines in SQL & KQL and extended distributed C# monitoring across petabyte-scale Azure storage, using statistical analysis to find latency bottlenecks and cut storage access latency.

   • Anomaly detection
   • SQL
   • KQL
   • C#
   • Azure

4. Sept 2021 – May 2022

   Stryker R&D

   Burnaby, BC

   Machine Learning Engineering Co-op, Computer Vision

   • Designed, trained, and evaluated CNN-based computer vision models in PyTorch for surgical video analysis, toward intra-operative complication prediction in complex, high-risk procedures.
   • Applied and evaluated explainable AI (XAI) techniques in PyTorch — including saliency mapping, feature attribution, and counterfactual reasoning — to improve model transparency and support surgeon decision-making.
   • Researched multimodal computer vision methods which integrate surgical video with structured clinical knowledge, to improve model robustness and interpretability in safety-critical, domain-specific settings.

   • PyTorch
   • CNNs
   • Computer vision
   • Interpretability (XAI)
   • Deep learning

5. May – Jul 2021 & May – Aug 2019

   Microsoft

   Virtual & Redmond, WA

   Software Engineering Intern & Explore Intern, Microsoft Azure Cloud Storage

   • Built low-overhead C++ latency-sampling tools and Python log-processing automation to tune Azure Cloud Storage performance (2021), and a modular performance-testing framework benchmarking the Azure .NET SDK (2019).

   • C++
   • Python
   • Azure

6. Jan – Apr 2019

   Samsung R&D Canada

   Vancouver, BC

   Product Management Co-op

   • Researched AI/ML paradigms and emerging frameworks to inform Samsung's enterprise product strategy and roadmap planning.

Education