SurrealDB

Cloud-native, multi-model database platform

Forward Deployed Engineer - Agentic AI

AI Application EngineeringUpdated on Nov 14

Full-timeNo salary listed

About the Job

Requirements

In-depth hands-on knowledge of SurrealDB; community contributors and SurrealDB Ambassadors are encouraged to apply.
Proven experience delivering AI or agentic systems and/or cloud data platforms in production.
Strong technical background in data modelling, cloud deployment (ideally Azure) and performance optimisation.
Experience working embedded in a customer or client engineering team.
Ability to clearly explain technical decisions to engineers and stakeholders.

Responsibilities

Work directly inside a customer’s engineering team, turning architecture into running production systems and leaving the team able to own the platform.
Perform hands-on implementation alongside the customer's engineers, from architecture through to go-live.
Own SurrealDB data modelling, deployment on Azure, data pipelines and migration.
Lead agent memory implementation, retrieval design and agent orchestration, such as Azure OpenAI.
Build evaluation, guardrails, observability and performance tuning into production systems.
Document architecture decisions, progress and time for weekly status reporting.
Contribute to knowledge transfer through pair programming, reviews and workshops.

Desired Qualifications

Familiarity with agent memory, agent frameworks, retrieval-augmented generation (RAG) and evaluation workflows is a strong plus.

SurrealDB is on the lookout for experienced engineers to join our bench of Forward Deployed Engineers, working embedded with enterprise customers to design, deploy and scale SurrealDB and SurrealDB Agent Memory in production. In this role, you will bring strength in agentic AI engineering.

As a Forward Deployed Engineer, you will work directly inside a customer’s engineering team, turning architecture into running production systems and leaving their team able to own the platform. You will report into a SurrealDB engineering manager and work closely with our engineering and customer teams.

About this engagement

Contract role, starting in early 2027 and running for around three months, full time
Open to applicants based in the UK or the US
Working US Eastern hours
You will work on customer-provided equipment and complete a background check before starting
Occasional on-site travel may be required
Our client will make the final selection. If you are not selected for this particular engagement, we will consider you for our bench of approved contractors for future

Ownership for hands-on implementation work alongside the customer's engineers, from architecture through to go-live
Responsible for SurrealDB data modelling, deployment on Azure, data pipelines and migration
Leading on agent memory implementation, retrieval design and agent orchestration (e.g. Azure OpenAI)
Building evaluation, guardrails, observability and performance tuning into production systems
Documenting architecture decisions, progress and time for weekly status reporting
Contributing to knowledge transfer through pair programming, reviews and workshops

In-depth hands-on knowledge of SurrealDB (community contributors and SurrealDB Ambassadors are encouraged to apply)
Proven experience delivering AI / Agentic systems and / or cloud data platforms in production
Strong technical background in data modelling, cloud deployment (ideally Azure) and performance optimisation
Experience working embedded in a customer or client engineering team
Ability to clearly explain technical decisions to engineers and stakeholders
Familiarity with agent memory, agent frameworks, retrieval (RAG) and evaluation workflows is a strong plus

The customer owns their build, production operations and go-live decisions; you guide, build alongside and transfer knowledge. Production issues go through SurrealDB Support as tickets, so engagement time stays focused on delivery.

About the Company

SurrealDB

Cloud-native, multi-model database platform

SurrealDB provides a cloud-native, multi-model database that unifies document, graph, relational, and time-series data in one platform. It runs on Rust and uses SurrealQL, a SQL-like query language, with real-time data synchronization and row-level security, and can function as both a database and a real-time API backend deployed from edge devices to multi-node clusters. The offering includes an open-source database and a fully managed cloud version, differentiating itself by replacing multiple databases and API layers in one system and supporting AI/ML workflows in real time. Its goal is to reduce backend complexity and development time for building scalable, real-time applications for developers and large enterprises.

Company size: 11-50
Company stage: Series A
Total funding: $54.9M
Headquarters: London, United Kingdom
Founded: 2020

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