Posting record
Open 12+ days
- Cygnify's site dates the posting
- Ghostless first sees this listing
- Last checked: still up
How we know
It hasn't been reposted, listed twice, or left up unusually long, and the posting doesn't say it's a talent pool.
Ghostless records the day it first sees a posting, so a repost can't reset its age. This one was already up when Ghostless started tracking Cygnify, so its true age is longer.
Last checked Nov 2, 2026.
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Upload your resumeWhat the posting discloses
Not stated: Pay, real vacancy, AI screening.
Why these three
Ontario requires postings from employers with 25 or more staff to state the pay, whether the vacancy exists, and whether AI screens applicants.
Some employers add pay only on their own site. Check the original posting before you rule it out.
Skills mentioned
About the role
Full Stack Engineer – AI
Role
We are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing how agents operate, fail, recover, and deliver consistent value to users.
Focus
- Build end-to-end product features across frontend, backend, and AI integrations
- Design agent workflows that handle planning, tool use, failure, and recovery across multiple steps.
- Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditions
- Design real-time AI interactions with streaming, partial results, and tight latency constraints
- Improve system reliability, observability, and fallback mechanisms
- Collaborate closely with ML, backend, and product teams to ship features end-to-end
- Continuously iterate based on real usage and failure modes
Ideal Experiences
- Strong experience in full stack engineering (frontend + backend)
- Solid understanding of system design and API architecture
- Experience working with LLMs, RAG systems, or AI-powered applications
- Ability to handle ambiguity and make pragmatic engineering decisions
- Strong ownership - able to take features from idea to production
- Comfort working in fast-moving environments with evolving requirements
Outcomes
- Own and ship AI-native product features that move beyond chat into persistent, goal-driven workflows
- Design and deploy agent workflows that reliably complete multi-step tasks across tools and sessions
- Reduce latency and improve responsiveness of AI interactions while maintaining output quality
- Build robust fallback and recovery mechanisms for LLM and tool failures in production environments
- Improve the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoring
- Establish patterns and abstractions for integrating LLMs, memory, and external tools into scalable product systems
- Contribute to a product experience where AI feels proactive, consistent, and dependable over time
Tech Stack
- Next.js
- Python
- NodeJs
- Pytorch
- OpenAI / Anthropic / open-source LLMs
- SQl & noSQL
- Kubernetes
- Docker
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