Individual placements
AI-skilled contractors, contract-to-hire candidates, and direct-hire placements embedded into your team under your management.
Core Services
AI Staffing Solutions
AI-skilled people and embedded teams, placed by a firm that also builds production AI.
The AI skills shortage is real, and it is not going to resolve on its own for several years. Spruce's AI Staffing Solutions practice helps clients close the gap two ways: individual AI-skilled contractors placed into your teams (contract, contract-to-hire, or direct hire), and embedded AI delivery teams that take on a defined scope of work under managed-capacity terms. Because Spruce also designs, builds, and operates production AI systems, every candidate we place has been evaluated against the standards of the work we do ourselves, not against an abstract checklist. You don't get résumés; you get AI practitioners who would be welcome on a Spruce delivery team.
Two ways to engage
Most clients need one of two things: a specific AI-skilled role filled, or a small team delivering against a defined scope. Our practice covers both:
Embedded delivery teams
A two-to-eight-person Spruce team embedded into your program, led by a Spruce delivery lead, with managed capacity and team-month pricing.
Choosing the right model
Both models are supported by the same vetting standard and the same bench. The right choice depends on whether you need capacity inside your own delivery structure or a self-contained team that owns a scope of work.
Individual placements
When your roadmap, architecture, and team structure are in place and you need capacity inside your existing delivery model, individual placements are usually the right fit. We place against the roles our clients actually need on modern AI programs:
AI/ML engineers
Model implementation, fine-tuning, evaluation harnesses, and production deployment.
Generative AI engineers
LLM application development, RAG systems, agent frameworks, and prompt engineering at production quality.
Data engineers and MLOps engineers
Pipelines, feature stores, model registries, and the deployment infrastructure that keeps models reliable in production.
AI solution architects
System design across data, model, application, and integration layers.
Applied data scientists
Problem framing, feature engineering, model selection, and evaluation.
AI product and program managers
Experienced at running AI delivery with the cadence and risk discipline the work requires.
AI UX designers
For copilots, assistants, and AI-assisted workflows where interaction design is the differentiator.
AI governance and responsible-AI specialists
For organizations standing up policy, risk, and oversight capabilities.
Engagement models for placements
Engagement models include contract, contract-to-hire, and direct-hire placement. We honor whichever commercial model fits your procurement and workforce strategy, and we're transparent about our placement terms up front.
Embedded AI delivery teams
When your roadmap is ahead of your team's capacity, or when you want a self-contained group that owns a scope of work end-to-end, an embedded delivery team is usually the better fit. A Spruce embedded team typically includes a lead architect or delivery lead, one or two AI or generative AI engineers, a data engineer, and (where appropriate) a UX designer or applied data scientist. The team integrates into your program cadence, attends your standups and reviews, and is measured against outcomes your leadership cares about. Capacity can ramp up or down as your roadmap evolves.
Embedded teams are priced by team-month under a managed-capacity agreement, which keeps the commercial model simple and predictable. When a scope is well-defined, we also support fixed-fee engagements. Either way, the Spruce delivery lead is accountable for the team's output, and Spruce's broader architecture and delivery organization stands behind the work.
Vetting and quality
Every candidate placed by Spruce, whether individual or as part of a team, goes through the same vetting we apply to our own delivery hires:
- Technical evaluation against production AI patterns (not toy problems) with a hands-on exercise reviewed by a Spruce architect.
- Portfolio review focused on real production work, with particular attention to how the candidate handled data, evaluation, and failure modes.
- Responsible-AI sensibility — awareness of bias, explainability, and human-in-the-loop design patterns.
- Communication and delivery discipline — because a brilliant engineer who can't move a team forward is a liability, not an asset.
- Reference checks against prior AI delivery engagements, not just generic software work.
Skills and technologies we cover
Our bench covers the technologies our clients actually run, across six capability areas:
Clouds
Azure, AWS, and Google Cloud.
Models
Azure OpenAI, OpenAI direct, Anthropic Claude, Google Gemini, Cohere, plus open-source Llama, Mistral, and Qwen.
Managed platforms
Azure AI Foundry, AWS Bedrock, and Google Vertex AI.
Stacks
.NET, Python/FastAPI, Node.js, Java, and Vercel/Next.js.
Frameworks
LangChain, LlamaIndex, Semantic Kernel, and the major agent frameworks.
Identity and data
Microsoft Entra for identity, and Azure Synapse, Microsoft Fabric, BigQuery, Redshift, and open-source lakehouses for data platforms.
Compliance, IP, and data protection
AI staffing engagements operate under the same contractual discipline as our build engagements. That includes clear IP assignment terms, background-check and compliance posture appropriate to your regulatory environment (CJIS, HIPAA, FERPA, SOC 2, FedRAMP, and state privacy regimes), and data-handling controls that match the sensitivity of the data your AI systems will touch. Contract-to-hire and direct-hire placements include structured transition support so the candidate ramps productively on your side from day one.
Who we serve
- Enterprises scaling an AI program faster than internal hiring can keep up.
- Public-sector agencies that need AI-skilled contractors cleared to operate in regulated environments.
- Healthcare, financial services, and education organizations with strict compliance posture and specialized skill needs.
- AI-native product companies that need a burst of specialist capacity during a build or launch.
- Clients running a Full AI Implementation or AI Solutions Development engagement with Spruce who want to stand up internal capacity in parallel.
Ready to move forward?
Every Spruce engagement begins with a short conversation about your goals, constraints, and timeline.