Posting record
Open 10+ days
- Monterail'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 Monterail, 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
We're building a network of LLM Engineers who can design, build, and integrate practical AI features into existing products. We're looking for people to collaborate with on a freelance basis - part-time or full-time, depending on project needs.
This role is focused on delivery, not ML research. What matters most is your engineering foundation and your ability to integrate AI into real production systems using Python, Node.js, or Ruby codebases.
Requirements
What we’re looking for
- Strong software engineering background - Python, Node.js, or Ruby
- Hands-on experience integrating LLM APIs into production systems (OpenAI, Anthropic, or similar)
- Ability to design pragmatic AI solutions within existing architectures
- Experience building RAG pipelines, embeddings, and vector search
- Understanding of cost, latency, and reliability constraints of AI systems
- Ability to operate independently with clients, take ownership of delivery, and set realistic expectations
- Background in big data or data pipelines is a big plus
- English B2/C1
- Availability of 15-40 hours per week (B2B contract)
What you'll do
- Deliver AI functionality within existing Python, Node.js, and Ruby codebases
- Build LLM-powered features: chat, summaries, classification, smart search, document Q&A
- Design lightweight RAG pipelines using embeddings and vector search
- Leverage vector DBs (pgvector, Pinecone, Qdrant)
- Implement safe, reliable LLM endpoints (OpenAI, Anthropic, Azure)
- Collaborate directly with clients to shape AI features and reduce manual effort
- Advise clients when NOT to use AI and navigate trade-offs around latency, accuracy, and cost
What do we mean by freelance?
- Read more at Monterail Tech Network
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