An expert engineer integrated into your AI strategy

The Forward-Deployed Engineer is a specialist who works closely with the client's team, bridging business and technology needs to accelerate the implementation, deployment, and adoption of AI initiatives. Through co-creation and knowledge transfer, the FDE also helps the client build the autonomy to evolve their strategy. This professional operates according to the guidelines of AI Foundation—Sensedia’s proprietary AI consultancy methodology—designed to help companies transform their AI initiatives into tangible business capabilities.

An FDE combines five domains: software engineering and architecture (production, distributed architectures, cloud, containers, and CI/CD); applied AI as an accelerator for analysis, construction, and validation; APIs, integrations, and platforms—including gateways, legacy systems, and MCPs (Model Context Protocols); security, governance, and observability; and technical communication with the client—translating problems into actionable plans and analyzing the trade-offs of various decisions alongside technical and business teams. Their key differentiator lies in the blend of technical depth and a pragmatic approach to delivery.

They are engineers and architects who already possess a senior level of expertise when they take on the role. Their training focuses on three areas: field experience on real-world projects, working alongside more seasoned professionals; specialization in the Sensedia platform and ecosystem—covering everything from APIs and integration to AI, governance, and observability; and continuous knowledge sharing across teams, where patterns and lessons learned from each project are fed back into Sensedia and made available to all other FDEs.

Their scope, their unique combination of knowledge and experience, and their close alignment with the product. A Sensedia FDE works directly on the code, carries the solution through to production, and supports its adoption—with observability and governance built in from the design phase. They bring Sensedia’s deep expertise in APIs, integrations, and complex digital ecosystems. Furthermore, they transform every field-learned insight into a reusable pattern and provide feedback to the Product, Consulting, and Customer Success teams, ensuring that every client benefits from solutions developed for previous clients.

Yes. The size of the team is determined based on your specific needs. A well-defined challenge is typically handled by a single FDE. Initiatives involving multiple simultaneous workstreams may require more than one professional with complementary skill sets. We define this with you at the outset and review it as the initiative evolves.

Both options are possible, as is a hybrid approach, depending on the project's needs. In any case, we maintain close collaboration with your teams, utilizing short cycles and shared responsibility. The working model is determined at the start of the engagement, taking into account the specific context, access restrictions, and delivery pace.

No. The FDE model is one of the offerings within our Strategic Consulting portfolio; it is best suited for situations involving a specific technical challenge, a use case that needs to move beyond the pilot stage, a complex integration, or an AI initiative that is struggling to reach production.

Sensedia combines specialized consulting, integration expertise, and technology to support end-to-end AI adoption. Our specialists work alongside the client on strategy, architecture, and implementation, connecting AI agents and models to the corporate ecosystem. This process is further enhanced by the Sensedia AI Gateway, which provides a central layer for governance, security, observability, and control of AI operations.

Through "MCP-ization," Sensedia prepares APIs and business capabilities so that AI agents can discover and utilize them in a standardized, governed manner. Our consulting team provides support throughout the process—from identifying the most relevant user journeys and APIs to designing the architecture, implementing MCP servers, and defining the necessary controls to securely connect AI agents to the corporate ecosystem with full observability.