Tianjing Li
Software Engineering Manager at Saris AI
Greater Montreal Metropolitan Area Canada
Summary
🤩
Rockstar🎓
Top SchoolTianjing Li is a Senior Software Engineer with a decade of experience building reliable backend systems, moderation pipelines, and full-stack features for large-scale consumer and enterprise products. Based in Montreal, she has led engineering efforts at startups and service teams, driving agentic fintech workflows at Saris AI and contributing to Cohere's open-source toolkit while previously shaping Trust & Safety infrastructure at Yik Yak and Sidechat. Her technical strengths center on Python, scalable APIs, Elasticsearch, and cloud platforms (GCP, AWS, Azure), with a track record of automating content detection that handled the majority of takedowns and slashed indexing times. Comfortable owning projects end-to-end, she pairs clear communication and scope discipline with hands-on implementation and stakeholder collaboration. A curious problem-solver, she steadily seeks challenging work and continuous learning—and is also a cat and snake dad, hinting at patience and an affinity for careful stewardship.
10 years of coding experience
8 years of employment as a software developer
Associate of Science - AS, PHYSICAL SCIENCES, Associate of Science - AS, PHYSICAL SCIENCES at Marianopolis CollegeBachelor of Applied Science (B.A.Sc.), Computer Science, Bachelor of Applied Science (B.A.Sc.), Computer Science at McGill University
English, French, Chinese
Github Skills (49)
connectors10
opensearch9
kmeans9
linear-regression9
opensearch-dashboards9
machine-learning-algorithms9
microsoft-teams9
application8
fine-tuning8
generative-ai8
llm8
crowd7
atlassian7
jaspersoft-studio7
ocr7
Programming languages (5)
Github contributions (5)
cohere-ai/cohere-toolkit
Apr 2024 - Mar 2025
Cohere Toolkit is a collection of prebuilt components enabling users to quickly build and deploy RAG applications.
Contributions:9 releases, 370 reviews, 320 PRs in 10 months
cohere-ai/quick-start-connectors
Dec 2023 - Mar 2024
This open-source repository offers reference code for integrating workplace datastores with Cohere's LLMs, enabling developers and businesses to perform seamless retrieval-augmented generation (RAG) on their own data.
Contributions:82 reviews, 29 PRs, 46 pushes in 3 months
retrieval-augmented-generationconnectorsraggenerative-aillm
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