Hire dedicated AI and .NET engineering teams
Add experienced AI and .NET engineers to your team without spending months recruiting hard-to-find specialists.
Get a dedicated team of pre-vetted ML engineers, LLM developers, computer vision specialists, and .NET developers who integrate into your workflows, follow your processes, and work toward your roadmap.
Microsoft Partner / ISO 27001 certified / 15+ years of engineering experience / 70+ enterprise projects
How Blackthorn Vision Builds and Scales Engineering Teams
Client / Industry: unSkript, incident response automation for DevOps and SRE teams, United States
Challenge: The client had a clear product vision for an AI-assisted platform that helps engineers diagnose and fix production incidents, and needed an engineering team that could build it without a long ramp-up. Hiring that team in-house would have delayed the product by months.
Team composition: We assembled a dedicated team of front-end and back-end engineers and a UX/UI designer, working alongside the client’s in-house people, while keeping ownership of QA and user testing. The designer mapped user flows and built interactive prototypes the client’s team used in usability testing, and development ran from there.
Result: The platform runs automated health checks on applications and API services, and the AI assistant suggests remediation steps that previously needed a DevOps specialist each time. The engagement has run since 2018, through several product phases, with the same team.
Why hiring AI and .NET engineers is broken
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The talent pool is too small while the demand is growing
AI and machine learning specialists are among the fastest-growing roles, and supply has not caught up with demand. 63% of employers name skills gaps as the main barrier to changing how their business works, and two-thirds said they are looking for people with AI skills. Filling one seat for a senior AI candidate can take a quarter or longer.
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Freelancers and generic outsourcing stop at the demo
A model that performs well in a notebook is still far from becoming a reliable service for your business. Production AI requires MLOps, model-drift monitoring – the process of detecting when accuracy declines as data changes – a fallback for when the model gets something wrong, and clear ownership of the system years after launch. Short freelance contracts don’t cover that full lifecycle.
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Compliance is now part of the engineering job
The EU AI Act has been in general application since September 2026, including transparency rules for AI-generated content. European Commission’s implementation timeline Documentation, logging, and human oversight must be designed into the system from the start. If your engineer has never built to those requirements, you may end up rebuilding the system years later.
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AI and .NET expertise rarely come together
Most AI vendors have limited .NET experience. Most .NET shops, meanwhile, have little experience taking AI models into production and keeping them accurate over the long term. When you split the work between two suppliers, neither owns the full problem. Integration issues fall into the gap between them, and every model update can turn into a scheduling negotiation between two companies.
Our dedicated AI and .NET engineering services
Dedicated AI and .NET engineering services
Hire a dedicated AI development team with machine learning, LLM, computer vision, and .NET expertise under one roof. We thoroughly screen all engineers, and every team works within our Microsoft Partner and ISO 27001 processes.
Hire an AI developer for a single role, or bring in a complete cross-functional team with QA and project management included.
Hire ML engineers
Production ML engineers for predictive analytics, MLOps, and model deployment. They build the training pipeline and the monitoring that keeps the model honest after release.
Hire AI / LLM developers
LLM fine-tuning, RAG pipelines that ground answers in your own documents, and agentic workflows built on Azure OpenAI and Semantic Kernel. This service suits teams that trust a language model with real business data.
Hire computer vision engineers
Object detection, video analytics, and industrial vision work with OpenCV and YOLO. These engineers have run models on real production lines, in changing light, on products that keep being redesigned.
Managed .NET dedicated team
Senior .NET engineers for enterprise applications and for the integration work that puts an AI model into daily use. This team brings 15 years of ASP.NET Core, Azure, and legacy backend experience.
Build-Operate-Transfer
We build your offshore AI and .NET team, run it for an agreed period, then transfer the whole unit to you as your own legal entity and staff. At the end, you own the capability completely.
Team Extension
We add pre-vetted AI or .NET engineers to the team you already have—your process, your backlog, your standups, with our people filling the gaps.
Why enterprises choose Blackthorn Vision to hire dedicated AI developers
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Microsoft Partner and ISO 27001 certified
Blackthorn Vision is a Microsoft-partnered .NET and AI development company that helps enterprise teams build and modernize complex software products. For healthcare, fintech, and industrial clients, the security certification and the Microsoft partnership settle the procurement questions before the technical part.
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AI and .NET in one team, so nothing gets lost between vendors
The same team builds both the model and the .NET application that runs it. There is no handoff between a data science supplier and an implementation supplier, and no extra meetings to figure out which part owns the bug.
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Pre-vetted engineers, in your standup in two weeks
Every engineer in our pool has completed architecture deep-dives, MLOps maturity checks, and live code reviews. We only offer them to a client after these checks. We do that work in advance, which is why you can hire AI engineers from us in two weeks instead of running a long and complicated search yourself.
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Production-first engineering
The system needs to show when predictions start getting worse, let the team compare a new model against the one already running, and switch to a safer fallback when the model fails. That means MLOps, observability, drift detection, and controlled model testing are part of the application from the start. It’s not something you do when the first production problem appears.
Dedicated AI and .NET engineering team
All technologies
Meet the dedicated AI and .NET engineering team at Blackthorn Vision
“We hire against the work our clients need done. Before an engineer joins a project, they have to show they can take a model from training to a working .NET service, not just build a model in isolation. That matters because our clients usually need one engineer who understands both sides: how the model behaves and how the production application around it needs to work.”
Mykhailo Terentiak, Founder & CEO
How we build your dedicated AI and .NET engineering team
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01
Requirements assessment
We go through the roles you need, the stack, the seniority level, the timezone overlap you want, and how long you expect the engagement to run. Deliverable: a team composition proposal with roles, rates, and a start date.
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02
Candidate matching
We select from our pre-vetted pool and run a technical screen against your specific stack and domain. Deliverable: two to three candidate profiles per role, with code samples or project history.
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03
Client interviews
You interview the candidates yourself and we facilitate the scheduling and any technical exercise you want to set. Deliverable: your selected engineers, with start dates confirmed.
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04
Onboarding
We handle tool access, repository setup, and sprint integration on our side, so the engineer joins your existing process instead of running a parallel one. Deliverable: the engineer in your standup from week two.
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05
Ongoing management
We run performance reviews, scale the team up or down as your roadmap moves, and replace anyone who is not working out. Deliverable: a monthly team health report and a replacement guarantee.
Let’s build your AI and .NET engineering team
Daryna Chorna
Customer success manager