Location: Toronto, Ontario
Our client is a fast-growing, venture-backed B2B SaaS company building an AI-powered competitive intelligence platform that helps revenue teams win more competitive deals. The company is AI-first internally, and its product is trained in part on proprietary data unavailable anywhere else on the open web.
About the role:
We're hiring a Senior Software Engineer to build and optimize state-of-the-art LLM-powered agents that can reason, plan, and automate workflows for users. You'll lead the design and development of search and retrieval agent systems, owning projects end-to-end from architecture through production readiness. You'll shape how we integrate retrieval-augmented generation (RAG), dense retrieval, query understanding, and agentic reasoning to deliver fast, accurate, and trusted search experiences at scale.
What you'll do:
• Build and ship backend systems powering agentic workflows — retrieval pipelines, orchestration layers, and multi-step agent architectures that turn millions of data points into actionable intelligence.
• Own evaluation of agentic systems at scale — build and operate evaluation frameworks (automated, offline, human-in-the-loop) measuring relevance, quality, latency, and end-to-end task success.
• Design and optimize retrieval and ranking systems — hybrid retrieval, re-ranking, query rewriting, and post-retrieval synthesis, with a clear grasp of the tradeoffs between BM25, dense retrieval, and hybrid approaches.
• Improve LLM-powered workflows end to end — prompt design, retrieval strategy, caching, and latency optimization.
• Ship with the customer in mind — connect technical decisions to customer outcomes and business impact, iterate quickly, and course-correct based on measured results.
• Collaborate across product, infrastructure, and data teams, and help establish patterns/best practices for production-grade agentic systems.
• Stay current on advances in LLMs, retrieval architectures, and agentic reasoning.
Must Have Skills:
• Production experience with backend systems — search/retrieval, data pipelines, distributed systems, or API-heavy services.
• Hands-on experience building or improving retrieval, search, or ranking pipelines.
• Experience building and/or evaluating agentic or LLM-powered systems (RAG, multi-step agents).
• Strong Python and software engineering fundamentals (testing, CI/CD, observability).
• Experience with vector databases (FAISS, PGVector, Pinecone, Weaviate, Elasticsearch, or OpenSearch).
• Experience with cloud infrastructure at scale (AWS, GCP, or Azure).
• Regular use of AI coding tools (Copilot, Cursor, Claude Code, or similar).
• Track record of shipping features tied to real user/business outcomes.
• Ability to own a project end-to-end and provide technical direction.
Nice to Have Skills:
• Experience designing multi-agent systems or complex orchestration workflows.
• Background in conversational search or dialogue systems.
• Open-source contributions in search, retrieval, or the LLM ecosystem.
