Supra ITs builds an enterprise agent platform that customers run in the cloud or on-prem. End users chat with those agents over documents, SQL, policy RAG, and operational systems (e.g. ticket triage, order audit, product intelligence).

You will own agent runtime and AI-adjacent backend: how skills are built, how tools are called, how retrieval and file context work, and how LLM traffic is routed, attributed, and kept safe. You will ship with Test Driven Development, architecture decision records, and story-level acceptance criteria — not notebooks that never reach production.

LangGraph / Deep Agent graphs (ReAct, multi-step research, custom skills)

Tooling: native file/SQL/todo tools, MCP (policy search, Graph files, customer systems)

Qdrant collections per agent, hybrid/keyword fallback, knowledge ingest vs chat attachments

AI Gateway: Agent Chat, Admin Builder, Sandbox, entitlements/deploy

Frontend (React/TypeScript) is not the primary hire, but you will touch chat/sandbox contracts when the agent loop or upload path changes.

Design and implement agent loops: system prompts, tool selection, state (messages, virtual FS, conversation persistence), streaming SSE

Improve retrieval quality (chunking, embeddings, hybrid search, citations grounded in tool output)

Keep chat attachments, durable conversation artifacts,



and knowledge corpus on the correct stores (they are not the same)

trace work to user stories and ADRs

Partner with Product, Architect, QA, and UX on sandbox-first proofs before entitle/deploy

Solid Python in production services (FastAPI or similar)

Hands-on LLM application work: function/tool calling, RAG, prompt + eval loops — not only wrapping a chatbot widget

Solid SQL / Postgres; TDD habit: you write the failing test first for services that can lie to users (file extract, persist, authz)

3–5+ years] software engineering, including [1–2+] years shipping LLM features

Vector DBs (Qdrant), embeddings pipelines, hybrid/BM25 + dense retrieval

React/TypeScript (Vite) enough to wire FormData, SSE, and admin sandbox

Knowledge graphs / Neo4j (Product Intelligence / Deep Research)

Docker/Kubernetes, AWS and/or GCP, on-prem install empathy

Domain exposure: manufacturing/distribution, ticketing (ConnectWise-class), policy/audit workflows

Training or fine-tuning foundation models as the main job

Pure data science / notebook research with no production bar

Prompt-only “AI consultant” without owning the runtime

Stories → ADRs → TDD implementation → review → QA. You will be expected to prove behavior in Admin Sandbox the same way production Chat works.

Skills: langchain,sql,python #

📌 AI Engineer (Mississauga)
🏢 Supra ITS
📍 Mississauga