Senior Software Engineer, AI

GuruLink; Toronto; Hybrid; CAD 110,000 - 170,000; Full time

Job summary

GuruLink is helping a Toronto-based, fast-growing B2B SaaS company hire a Senior Software Engineer to build state-of-the-art, LLM-powered agent systems that reason, plan, and automate workflows. You’ll own architecture through production, integrating retrieval-augmented generation and dense retrieval for fast, trusted search at scale.

You’ll design and optimize the retrieval and ranking pipelines, collaborate with product, infra and data teams, and relentlessly improve latency and quality for

Qualifications

• 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.

Responsibilities

• 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.

Skills

Backend systems

Retrieval pipelines

Agentic/LLM systems

Python

Vector databases

Cloud infrastructure

AI coding tools

Project ownership

Tools

FAISS

Pinecone

Elasticsearch/OpenSearch

Weaviate

Job description

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.

This role is hybrid onsite Monday, Wednesday and Thursday every week at their office in downtown Toronto.

About the role:

We're hiring a Senior Software Engineer to build and optimize state-of-the- 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.