Hire dedicated LLM developers who build enterprise AI that holds up once it is in production. Azure OpenAI, Semantic Kernel, RAG pipelines, and AI agents, wired into the .NET applications and cloud infrastructure you already run.
Microsoft Partner / Azure OpenAI / ISO 27001 / Onboard in 2 weeks
A large language model can draft, classify, summarize, and answer questions about your business. It can only do that well when it can reach your data and your systems, and that is the hardest part. Companies hire LLM engineers who write excellent prompts, but not always can connect the model to an ASP.NET Core application or an ERP the company has run for years.
We staff the whole job.
Our LLM developers work in Azure OpenAI and Semantic Kernel. The same people write the .NET code that carries the model’s output to a user. Blackthorn Vision has been a Microsoft-stack engineering company since 2009 and holds ISO 27001. Security is a priority for us, and we’re happy to answer any questions you have during the initial call.
Many developers have built an LLM feature by connecting an application to an API. Far fewer have run a retrieval pipeline within a strict latency budget, investigated why token usage tripled after launch, or measured hallucinations against a properly labeled evaluation set. Those problems appear when an LLM system moves to real users, and they are incredibly hard to assess from a CV alone.
Most enterprise data it scattered. Contracts sit in SharePoint, customer records in the CRM, transactions in a .NET backend, and many nuances — in emails, notes, and heads. An LLM that can’t access those systems may handle general questions well, but it can’t answer questions using the reliable data. Access requires authentication, data integration, permissions, APIs, and the LLM layer to work together, and most importantly, an expert who understands all sides of this process.
An AI agent that can call tools, maintain context across multiple steps, and take actions in live systems requires more than good prompting. It’s a software engineering area. You need to define what each tool can do, what happens when a call fails, when an action should be retried, where a human must approve it, and how much the agent is allowed to spend. Those decisions need to be made before the agent moves to production, because a system that can take action also needs clear limits when the model makes an incorrect decision.
The term “LLM developer” can describe very different levels of experience. One specialist may focus on connecting an application to a hosted model through an API; another — on building RAG systems with a production vector store, evaluated models against datasets, and fine-tuned open-weight models when the use case justified it. Enterprise projects usually need several of these skills, along with the governance and controls required for production. It’s difficult to figure out what exactly you need, and even harder — to assess those skills.
From Blackthorn Vision, you can hire dedicated LLM developers who build enterprise AI on Azure OpenAI, Semantic Kernel, and retrieval pipelines grounded in your corporate data. Clients hire LLM engineers from us for one component or for a full case that covers modeling, evaluation, integration, and deployment. Every engagement starts from a thorough evaluation of your stack, data, and compliance nuances.
Retrieval-augmented generation, or RAG, looks up the answer in your documents and has the model write from what it found. We handle the parts that decide whether it works: how documents are split, which vector store fits your data volume, how permissions carry through to retrieval, and how you measure answer quality over time. Our LLM and RAG development service page covers the architecture in more depth.
We build multi-step AI agents with Semantic Kernel that can call tools, retain context, and coordinate actions across your existing systems. Each agent has clearly defined limits on what it can do without approval and which actions require a person. We also log tool calls, inputs, outputs, and decisions so your team can trace the agent’s activity for review or investion.
Fine-tuning makes sense when prompting and retrieval are no longer enough. Usually, when you need better performance on domain-specific language, a consistent output format, or a particular style. We build the evaluation process around it as well: labeled test sets, hallucination measurements, and regression checks before each model change. That gives you a clear baseline and a measurable way to determine whether a new model or fine-tuning run in fact improved the system.
For the model to be useful it needs to work inside the software that’s already in use. We integrate LLMs with ASP.NET Core applications, ERP and CRM platforms, and legacy services that were built with no AI in mind. Azure Functions handle background processing, while SignalR supports applications that need responses to stream in real time. You receive an AI feature inside your existing software.
Our engineers work with Azure OpenAI Service, Semantic Kernel, and Azure AI Search as part of production projects. For companies already running on Azure, this means the LLM team can work within your existing tenant, identity model, security controls, and data residency.
Blackthorn Vision is a Microsoft-partnered .NET and AI development company focused on building and modernising complex software products. We work under ISO 27001 processes and NDA requirements, on projects involving sensitive business or public-sector data. For enterprise LLM systems, we also account for applicable EU AI Act requirements and build the necessary logging, documentation, and human oversight into the system.
We build Semantic Kernel agents with tool calling, human approval points, exception handling, and cost monitoring from the start. Token limits and usage alerts are defined before an agent reaches staging, while logs make it possible to trace tool calls and investigate unexpected behaviour in production.
LLM projects often involve two separate skill sets: building the AI functionality and integrating it into the application. When those responsibilities sit with different suppliers, a handoff between teams can be problematic. Our LLM developers also work with ASP.NET Core and .NET, so the model, APIs, and application around it can be developed by one team.
“You need to treat an agent with write access to your systems like any other production service that can change data. We apply the same code review, logging, access controls, and rollback procedures we would use for a critical business application. If an agent can create records, update systems, or trigger actions, setting up clear permissions and an audit trail for every action is or non-negotible and high-priority task is to.”
Mykhailo Terentiak, Founder & CEO
01
We start with outlining and analyzing what you want the LLM to do, what data it needs, where it will run, and which .NET or other systems it needs to connect to. We also discuss the timeline, required seniority, and whether you need one specialist or several, or a full team. You then get a clear scope covering the roles, engagement, rates, and expected start date.
02
Candidates go through an LLM architecture review, a RAG design challenge, a prompt engineering exercise, and a .NET integration scenario based on real project requirements. This helps us assess their skills beyond the ability to work with an LLM API. We look at whether they can design, integrate, and operate an LLM system in a production environment. We then send you a short list of vetted candidates with their assessment results.
03
You interview the shortlisted candidates and make your final choice. For most clients, one technical conversation is enough. They also discuss communication, working style, and cultural nuances. We coordinate the interviews, answer questions about the assessments, and arrange another candidate if the shortlist does not meet your requirements.
04
Once you choose your engineer or a team, we handle the initial setup: Azure access, repository permissions, development tools, and integration with your sprint process. Your engineer gets access to the required repositories, tools, and documentation, then starts working within your existing sprint and review process.
What is the difference between an AI developer and an LLM developer?
An AI developer is a broad title covering machine learning, computer vision, and data work. An LLM developer specializes in large language models: prompting, retrieval architecture, fine-tuning, evaluation, and the orchestration frameworks that turn a model into an application. If your project centers on text, documents, chat, or agents, the LLM specialization is the one you want.
Both are available. Dedicated means engineers who join your team and stay for months or years, which suits ongoing product work. Project-based suits a defined build with an end date, such as a RAG pipeline over one document set. Many clients start with a scoped project and move to a dedicated team once the direction is settled.
Yes. Our engineers work inside your existing solution and respect the architecture already there. The model connects through your service and authentication layers, with no separate Python service bolted on the side.
We ground answers in retrieved source passages and cite the document each claim came from, so a reviewer can check it. Where the output feeds another system, we constrain it to a defined schema. Every prompt or model change then runs against a labeled evaluation set before it ships. For high-stakes steps, we add a human approval point.
We build custom pipelines. Off-the-shelf RAG tools are useful for a proof of concept, but they often lack permissions, mixed document formats, and answer-quality measurement. We design chunking, retrieval, and evaluation around your actual documents, using managed services such as Azure AI Search where they fit.
Yes, it’s our strongest area. We are a Microsoft Partner, and most of our LLM work runs on Azure OpenAI Service with Semantic Kernel and Azure AI Search, deployed inside the client’s own tenant. That covers data residency, private networking, and identity through Entra ID.
