Senior engineers from encorp.ai's Sofia team design, build and operate your AI agents: agent architecture, retrieval, evaluations, MCP integrations, cost control and EU AI Act governance. They work alongside your own developers, so the know-how stays in your company after go-live.
Models and practices we engineer with
An AI Frontier Engineer is a senior software engineer who turns frontier models such as GPT-5.x, Claude and Gemini into AI agents and automations that run reliably in production, owning the architecture, retrieval, evaluations, integrations and guardrails. As of September 2026 there is no single standard definition of the role; this is the working definition encorp.ai uses.
Two developments show why the role now matters. Microsoft's 2025 Work Trend Index (April 2025) described the "Frontier Firm": an organisation built on intelligence on tap, where people and agents work in mixed teams and every employee becomes an "agent boss". In June 2026 Cognizant introduced a "Frontier Certified Engineer" role for people who build agentic systems, retrieval and context layers, and multi-agent pipelines in production. Both point to the same gap: companies need engineers who can make frontier models do dependable work.
"Frontier" describes what the engineer works on: the newest models and the agents built on them. "Forward deployed" describes where an engineer works: embedded inside a customer's team. The two often overlap, but they answer different questions. This page is about the role and its skills; our Forward Deployed Engineers page covers the embedded engagement model.
At encorp.ai, AI Frontier Engineers are senior engineers from our Sofia team who design, build and run agents for companies in Bulgaria and the EU, and who work hands-on with your developers so they can maintain and extend what we build. "AI Frontier Engineer" is a generic role description, not a brand or a certification, and encorp.ai is not affiliated with Microsoft or Cognizant.
An agent prototype takes an afternoon. Production takes a different kind of work: grounding answers in your own data, testing every change against an evaluation set, keeping token spend predictable, restricting what the agent is allowed to do in your systems, and documenting it all for the EU AI Act. Classic development teams and data science teams usually cover part of that skill set, rarely all of it — so many agent projects never leave the pilot stage.
These are the areas our engineers cover on every agent we build — and the areas we coach your developers in while we build it.
Choosing between a single agent, a workflow with model steps or a multi-agent pipeline; tool calling, state, retries, timeouts and human approval checkpoints.
Chunking, hybrid search and reranking, permission-aware retrieval, citations, and a context window assembled deliberately for each step instead of stuffed.
Task-specific evaluation sets, automated scoring on every prompt or model change, input and output guardrails, and red-teaming before anything goes live.
MCP servers that expose your ERP, CRM and internal APIs to agents with scoped permissions, and A2A connections where agents from different vendors must cooperate.
Model routing so simple steps use smaller models, prompt caching, batching, streaming, and a token budget per workflow with cost per task on a dashboard.
Prompt-injection defences, least-privilege agent identities, secrets handling, audit logs, risk classification and the technical documentation the EU AI Act expects.
Typical builds for companies in finance, operations and customer service. Each starts with a defined process, a baseline and an evaluation set agreed with the business owner.
One agent gathers financials and policy, another drafts the memo in your template, a third checks it against credit rules. Every release is scored on an evaluation set before officers see a draft.
Sends each request to the smallest model that passes your quality threshold, caches repeated context and enforces a budget per workflow, with cost per task visible to finance and engineering.
Answers questions across your contract base with clause-level citations, respects document permissions and replies "not found" instead of guessing when the answer is not in the source.
An MCP server exposes approved actions in SAP, Microsoft Dynamics or another ERP, so agents can check stock, create orders or post documents with scoped permissions and an approval step.
An adversarial test run covering prompt injection, data leakage, jailbreaks and off-policy answers. Guardrails are added, the tests are repeated, and the findings go into your EU AI Act file.
Answers routine calls in your customers' language, verifies the caller, checks order or account status through tools and transfers to a person with a summary when confidence is low.
All three roles matter. The difference is what each is trained to optimise — and who owns an agent once it has to work every day.
| Traditional software developer | ML / data scientist | AI Frontier Engineer | |
|---|---|---|---|
| Optimises for | Correct, maintainable code to a specification | Model accuracy on a dataset | A business process that runs reliably on frontier models |
| Works with frontier models (GPT-5.x, Claude, Gemini) | Calls the API when a feature needs it | Often trains or fine-tunes own models; LLMs are one tool among many | Every day — selects, prompts, routes and combines them |
| Builds production agents | Rarely — builds deterministic software | Rarely — hands models over to engineering | Yes — tools, memory, orchestration and human hand-offs |
| Evaluations | Unit and integration tests for deterministic code | Offline metrics on a held-out test set | Task-level evaluation sets run on every prompt, model or tool change |
| Business ownership | Delivers against tickets | Delivers analyses or a model | Owns the process outcome together with the business owner |
| Governance & security focus | Application and infrastructure security | Data quality, bias and model risk | Prompt injection, agent permissions, audit trails, EU AI Act documentation |
Evaluations are written before the agent, your engineers pair with ours from the first week, and nothing reaches production without a red-team pass.
We pick one process with your business owner, agree how success is measured, draft the evaluation set, choose models and architecture, and check data access and EU AI Act risk.
Agent logic, retrieval and MCP or API integrations are built in a staging environment and scored on the evaluation set after every iteration. Your developers work in the same repository.
Red-teaming, guardrails, cost and latency tuning, then a supervised rollout where people review the agent's outputs until it meets the agreed quality bar.
AI-OPS monitoring of quality, cost and failures, re-running the evaluations whenever a model is updated, and structured hand-over so your team can own the agent.
Start with a free call. Scope and price for a sprint are fixed once we understand the use case, and model usage costs are estimated before the build starts.
All prices exclude VAT.
A senior engineer's view on your use case
30 minutes · online
One production agent, built with your team
fixed scope and price after the call
An ongoing AI programme with senior oversight
per month · minimum 6 months
Single, smaller automations start from €500 per automation. Model and cloud usage costs are not included in engineering prices. "AI Frontier Engineer" describes a role, not a certification; encorp.ai is not affiliated with Microsoft or Cognizant.
Selected case studies from our portfolio across fintech, telecom, logistics, media and e-commerce. Nikola Alexandrov, CEO of Hydra Blockchain, on the engineers onboarded through encorp: "The team members are not only skilled but also proactive."
Implemented a custom AI agent for Econt, the leading logistics provider in Bulgaria, to automate customer support.
Vivacom is one of the leading telecommunications companies in Bulgaria, offering a full range of services including mobile, fixed-line, internet, and digital television. As part of the United Group, Vivacom is recognized for its innovation, reliable network infrastructure, and customer-centric approach. The company serves millions of residential and business clients, delivering high-speed connectivity, advanced telecom solutions, and cutting-edge digital services across the country. With a strong focus on digital transformation, 5G technology, and smart business solutions, Vivacom continues to play a key role in shaping the future of Bulgaria’s telecom sector.
Delivered AI-focused educational trainings for Forbes Bulgaria to empower business leaders with practical knowledge.
Describe the use case or the prototype that is stuck. A senior engineer replies within one working day with a first view on architecture, effort and risks.
My experience with Encorp has been excellent, especially in terms of the quality and dedication of the engineers that we’ve onboarded through them.
Copilot Studio agents, agent flows and Power Automate automations built inside your Microsoft 365 tenant.
Microsoft Copilot AutomationPro-code AI agents and applications on Microsoft Foundry (formerly Azure AI Foundry), in EU regions.
Microsoft Foundry DevelopmentArchitecture and delivery across the Microsoft AI stack: Microsoft 365 Copilot, Copilot Studio, Foundry, Azure OpenAI and Fabric.
Microsoft AI PlatformGemini Enterprise agents and automations connected to Google Workspace, Microsoft 365 and your business systems.
Google Gemini EnterpriseForward deployed engineers embedded in your team to ship AI agents into your real systems in weeks.
Forward Deployed EngineersBook a free 30-minute call with a senior engineer. We will look at your use case, the architecture it needs and what it takes to run it safely in production.
650+ automations deployed · Sofia team since 2019 · Bulgarian and English · EU AI Act-aligned · Prices exclude VAT