Senior Forward Deployed Engineer

Blockbrain•Stuttgart, AR

35d•€75,000 - €110,000•Hybrid

About The Position

Blockbrain’s Forward Deployed Engineers are client-proximate technical operators who deploy, extend, and integrate the Blockbrain platform in client environments. The role owns delivery from signed contract through to a working system, with accountability for time-to-value. It is not a support role: the engineer writes production-grade code, designs integration architectures, and makes technical decisions with clients. Responsibilities span end-to-end platform deployment, custom client-specific extensions, retrieval and agent configuration, and support for Kubernetes-based and self-hosted deployments. The engineer works closely with client engineering teams and Blockbrain’s Product and Engineering teams, reports actionable product gaps from the field, and leads technical discovery, integration workshops, and architecture reviews. The role also applies AI coding tools and Blockbrain agents to accelerate delivery and shares real-world feedback on AI capabilities. The position is based in Stuttgart, with on-site work three days per week and flexible working hours. After probation, employees may work from abroad for up to eight weeks per year. Compensation is €75,000–€85,000 fixed salary plus €15,000–€25,000 variable compensation.

Requirements

At least 5 years of experience in solutions engineering, full-stack development, or a client-facing technical role, with demonstrated ability to deliver projects from ideation through deployment.
Professional German at B2 level or higher and English, with the ability to lead client consultations, demos, and technical workshops.
Strong TypeScript proficiency across the stack and willingness to learn new technologies and infrastructure quickly.
Experience or familiarity with TypeScript/Node.js, Python, REST APIs, GraphQL, Kubernetes, Docker, ArgoCD, Terraform, PostgreSQL, MongoDB, vector databases, LLM and embedding pipelines, and CI/CD tooling such as Sentry, Grafana, and Datadog.
Pragmatic, action-oriented approach, with the ability to prototype quickly, deliver client value, and work in ambiguous and dynamic environments.
Ability to manage multiple concurrent client engagements, coordinate across Product and Engineering, and keep deployments on track independently.
Degree in Computer Science, Software Engineering, or a related field, or equivalent practical experience.
Entrepreneurial, resilient, and goal-oriented mindset; independent problem-solving; ability to propose tailored solutions, experiment using client feedback, learn from challenges, and communicate technical concepts clearly to different audiences.

Nice To Haves

Familiarity with LLMs, data processing, and coding agents.
Hands-on use of AI-assisted workflows for software development, retrieval tuning, or test authoring.

Responsibilities

Own end-to-end delivery of Blockbrain in client environments, from infrastructure standup through agent configuration, retrieval tuning, and integration wiring.
Work with client engineering teams to ensure seamless adoption.
Develop client-specific connectors, API adapters, and workflow extensions using TypeScript/Node.js, Python/FastAPI, and Next.js.
Tailor the platform to client data models, authentication systems, and business processes.
Configure and optimize the knowledge retrieval layer, including vector indexes, reranker pipelines, and graph integrations.
Translate client domain requirements into retrieval architecture decisions.
Support and troubleshoot Kubernetes-based deployments, cloud integrations, and infrastructure as code.
Take ownership of clients running self-hosted Blockbrain instances.
Document patterns, edge cases, and missing capabilities so they can inform the Product and Engineering backlog.
Lead discovery sessions, integration workshops, and architecture reviews with client engineering stakeholders.
Own the technical narrative in client meetings and teach customer advocates to become platform experts.
Use Claude Code and Blockbrain agents to accelerate deployments, debugging, integration scaffolding, and documentation.
Experiment with AI-assisted code generation, retrieval tuning, and test authoring to reduce client time-to-value.
Provide the product team with field feedback on Blockbrain’s AI capabilities and apply emerging AI tools in client engagements.