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.