Building an AI Engineering Team Without Building an Entire AI Hiring Function
The client
A US-based SaaS company was expanding its product with AI capabilities and needed specialised engineering talent to move from experimentation to production.
The company already had a core product engineering team. What it didn't have was enough specialised capacity across AI/ML engineering, backend development and cloud infrastructure to build and deploy the new capability at the pace the product roadmap required.
- Industry B2B SaaS
- Stage Growth-stage
- Market United States
- Engagement Dedicated AI & Cloud Engineering Team
The objective
The objective wasn't simply: "Hire AI engineers."
The company needed to build the technical capability required to take AI features from development into a production environment. That meant finding engineers who could work across the surrounding engineering stack:
AI/ML → Backend → Data → Cloud → Deployment
The company wanted the offshore team to work alongside its existing engineering organisation rather than operate as a separate outsourced development function.
The challenge
AI projects create a different hiring problem from conventional product development. A company may already have strong backend and frontend engineers but still need specialised expertise in:
Machine learning
AI application development
Data pipelines
Model integration
Cloud infrastructure
Deployment and monitoring
Finding all of those capabilities internally can mean running several specialised hiring processes simultaneously. And for a growing SaaS company, every month spent assembling the team is another month the product roadmap waits.
The company therefore needed specialised engineering capacity without creating a completely new internal recruitment operation.
The True North approach
01 — Map the technical requirement
True North first broke the requirement down by capability rather than treating it as one broad "AI engineer" vacancy. The team structure was mapped around the actual product requirement:
- AI / ML Engineering — building and integrating the AI capability.
- Backend Engineering — connecting the AI layer with the existing product.
- Data Engineering — preparing and managing the data required by the system.
- Cloud / DevOps — supporting deployment, infrastructure and production environments.
This created a team around the AI product lifecycle, rather than simply supplying individual AI specialists.
02 — Recruit for the actual environment
True North's role was to source and screen engineers against the company's technical requirements. The focus was not just on whether someone had "AI" on their CV. The important question was: Can this engineer contribute to a production engineering environment?
That meant evaluating candidates against the relevant technical capabilities and the client's existing engineering setup.
03 — Keep the client in the hiring process
The client retained visibility into the people joining the team. Candidates could be evaluated by the company's own technical leadership before being selected.
This meant the client wasn't simply purchasing a pre-packaged engineering team. It was building its own team with True North providing the hiring infrastructure around it.
04 — Integrate with the existing engineering organisation
The offshore team was structured to work alongside the company's existing engineers. The internal team retained:
- Product ownership
- Architecture decisions
- Technical direction
- Roadmap ownership
The offshore team provided the additional engineering capacity required to execute against those priorities.
True North wasn't taking ownership of the product. It was helping the company build the engineering capacity to build it.
The result
The company gained access to a dedicated engineering capability across the AI and cloud layer without having to create every specialised role through separate internal recruitment processes. The resulting model gave the company:
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Specialised AI/ML capability
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Additional backend capacity
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Cloud and infrastructure support
-
A dedicated offshore engineering layer
…while keeping product and technical ownership with the internal team.
Why this engagement matters
AI adoption is creating a different kind of engineering hiring problem. Companies don't necessarily need hundreds of engineers. They increasingly need small groups of specialised engineers who can actually build, integrate and operate AI systems in production.
That makes the ability to assemble the right combination of engineering skills increasingly important. And that is where an offshore model can fit.
You keep the product. You keep the technical direction. You add the engineering capacity. True North builds the team around it.
Have a team to build?
Tell us what you're hiring for. We'll show you what an offshore team could look like.