Publié il y a 21 jours
Senior AI Software Engineer
@ Encore Technical Solutions Inc.
Toronto, Ontario
Résumé des exigences :
- Our client, a global enterprise, is building an internal AI platform — a multi-tenant, model-agnostic environment spanning a web app, a desktop agent client, an AI App Store, and a…
- Build MCP integration, sandboxing/process isolation, and a permission system (ALLOW/ASK/DENY) enforced across runtimes.
- Direct experience with at least one frontier provider SDK at the agent-loop level.
- Sandboxing/isolation experience (gVisor, nsjail, Firecracker, Seatbelt, Bubblewrap).
- Electron/desktop app engineering, vector search, regulated-industry experience (insurance, banking, financial services).
Source et dates
Type de source non vérifié · apify-linkedin
- Date publiée par l’employeur
- 2026-10-20
- Première détection par notre source
- 2026-11-11
- Mise à jour du dossier source
- 2026-11-11
Ces dates décrivent le dossier source, pas une vérification en direct du recrutement. Consultez l’offre originale.
Encore Technical Solutions Inc.
| Champ | Valeur |
|---|---|
| Offres sur Jobsy | 2 |
Senior Software AI Engineer — Agentic Platforms
Full-Time, Permanent | Downtown Toronto | Hybrid
Our client, a global enterprise, is building an internal AI platform — a multi-tenant, model-agnostic environment spanning a web app, a desktop agent client, an AI App Store, and a skills/tool ecosystem over the company's knowledge.
They're looking for a Senior AI Engineer to help design and build the agent infrastructure at the core of it: orchestration engines, the desktop agent harness, tool/skill registries, and the policy layers that make it all safe to run at enterprise scale.
What you'll do
- Build and extend multi-agent orchestration (LangGraph, Microsoft Agent Framework, Deep Agents) — stateful graphs, checkpointing, sub-agents, human-in-the-loop, long-running execution.
- Work directly with frontier LLM SDKs at the agent-loop level (Anthropic, OpenAI, Google) — not just chat completions.
- Own the model-agnostic routing layer: provider routing, cost/token accounting, graceful degradation as models churn.
- Build MCP integration, sandboxing/process isolation, and a permission system (ALLOW/ASK/DENY) enforced across runtimes.
- Build retrieval, grounding, and citation pipelines that hold up in production.
- Build eval infrastructure — regression gates, tracing, and diagnosability for agent failures.
What we need
- 7+ years building production software, with recent, substantial work on LLM-based systems used by real users.
- Strong TypeScript (Node.js, async, strict typing) and working proficiency in Python (FastAPI, Pydantic).
- Production depth in LangGraph, Microsoft Agent Framework, or Deep Agents.
- Direct experience with at least one frontier provider SDK at the agent-loop level.
- Hands-on with tool calling, MCP (or comparable), streaming (SSE/WebSocket), structured output.
- Cloud-native delivery: containers, Kubernetes (AKS preferred), CI/CD, observability.
Nice to have
- Google ADK, Semantic Kernel, CrewAI, LlamaIndex, AutoGen, Pydantic AI, Mastra.
- Sandboxing/isolation experience (gVisor, nsjail, Firecracker, Seatbelt, Bubblewrap).
- Electron/desktop app engineering, vector search, regulated-industry experience (insurance, banking, financial services).
Journal
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