Forward Deployed Engineer – Physical AI Cloud Platform

Jobtailor; Deutschland; Vor Ort; EUR 90.000 - 150.000; Vollzeit

Zusammenfassung

Jobtailor is seeking an experienced Cloud Infrastructure Engineer to design and deliver AI workflow platforms for strategic accounts in Germany. You will own end-to-end cloud architecture, drive multi-tenant SaaS, and partner with customer teams to accelerate deployments.

Strong backend and systems programming skills, Kubernetes, CI/CD, and AI tooling experience are essential, along with a track record in reliability, security, and cost optimization.

Qualifikationen

• 6+ years of hands-on engineering in backend/cloud/platform roles.
• Experience building distributed systems and job orchestration.
• Strong Python, Go, or similar systems programming skills.
• Fluency in AI coding tools (Claude Code, Codex, Cursor) for rapid design and deployment.
• Experience with Kubernetes, containers, CI/CD, observability, cloud networking, IAM/RBAC, and infrastructure as code.
• Familiarity with GPU workloads, batch jobs, training pipelines, or HPC environments.
• Ability to debug infrastructure issues across app, network, storage, compute, and orchestration layers.
• Security, reliability, and uptime focus for customer workloads.
• Excellent written and verbal communication.

Aufgaben

• Own end-to-end delivery inside strategic accounts from discovery to production rollout.
• Design and operate cloud infrastructure powering AI workflows.
• Build platform services for job execution, scheduling, retries, observability, logging, secrets, access control, and cost tracking.
• Develop onboarding infrastructure for pilots including sandbox environments.
• Optimize cloud cost, utilization, performance, and reliability across workloads.
• Collaborate with FDE for heavy simulation pipelines.
• Define long-term multi-tenant SaaS infrastructure architecture.
• Turn customer infrastructure pain into reusable platform capabilities.
• Leverage AI coding tools to accelerate development timelines.
• Create reference architectures and technical blogs for field support.

Kenntnisse

Backend development

Cloud infrastructure

Distributed systems

Systems programming (Python/Go)

AI-native development tools

Kubernetes / container orchestration

CI/CD & observability

Security & reliability

Communication

Customer-facing technical roles

Tools

Kubernetes

CI/CD

Cloud networking

IAM/RBAC

Observability tools

Storage solutions

Jobbeschreibung

Responsibilities

• End-to-End ownership inside strategic accounts: own discovery, technical scoping, infrastructure design, build, and production rollout for each design partner and ISV engagement.
• Cloud infrastructure & compute orchestration: build and operate cloud infrastructure that powers customer physical AI workflows.
• Platform services: build platform services for job execution, scheduling, retries, observability, logging, secrets, access control, and cost tracking.
• Customer onboarding infrastructure: build onboarding infrastructure for pilots including sandbox environments and secure execution.
• Reliability, security & cost: optimize cloud cost, utilization, performance, and reliability across workloads.
• Cross-FDE partnership: partner with Physical AI Systems FDE to support heavy simulation pipelines.
• Long-term architecture: define the long‑term infrastructure architecture for multi‑tenant SaaS and high‑throughput physical AI workloads.
• Pattern codification & productization: turn customer infrastructure pain into reusable platform capabilities.
• Rapid engineering velocity: use modern AI coding tools to compress build timelines.
• Field enablement & feedback loops: co-author reference architectures, solution templates, and technical blogs for broader field support.

Requirements

• 6+ years of hands‑on engineering: strong backend, cloud infrastructure, platform engineering, or SRE experience, with at least two years in a customer‑facing or deployment‑oriented technical role.
• Distributed systems & compute platforms: experience building distributed systems, job orchestration, compute platforms, internal developer platforms, or ML infrastructure.
• Strong systems programming: strong Python, Go, or similar systems and backend programming skills.
• AI‑native development workflow: fluency in modern AI coding tools (Claude Code, Codex, Cursor) as primary leverage to rapidly design, implement, test, debug, and refactor production‑quality software.
• Cloud‑native toolchain: experience with Kubernetes, containers, CI/CD, observability, cloud networking, storage, IAM/RBAC, and infrastructure as code.
• GPU & HPC workloads: familiarity with GPU workloads, batch jobs, training pipelines, inference workloads, or HPC‑style compute environments.
• Cross‑layer debugging: proven ability to debug infrastructure issues across application, network, storage, compute, and orchestration layers.
• Security & reliability instincts: strong instincts for isolation, RBAC, uptime, and traceability on workloads that touch customers.
• High agency: navigate ambiguity without waiting for permission, with a bias toward simple, composable infrastructure that serves real customer workflows.
• Communication: strong written and verbal communication for technical discussions.

Core Competencies

Demonstrates expertise in building and optimizing cloud infrastructure for AI workflows, with a strong focus on distributed systems, backend programming, and customer‑facing technical roles. Proficient in leveraging modern AI coding tools and cloud‑native technologies to enhance reliability, security, and performance across multi‑tenant SaaS environments.

Tools & Technologies

• Kubernetes
• CI/CD
• Cloud Networking
• IAM/RBAC
• Observability Tools
• Storage Solutions