Principal Platform Engineer, Agentic AI Infrastructure

esqLABs GmbH; United States; Remote; USD 140,000 - 200,000; Full time

Benefits offered by this job

Flexible hours

Home office policy

Team gatherings across Europe

Job summary

esqLABs GmbH is seeking a senior platform/infrastructure engineer to design and build the AI agent platform infrastructure for reproducible scientific modeling workflows. You will own release engineering, CI/CD, and secure, multi-tenant operations across deployments, with a focus on policy controls and toolchains.

The role emphasizes delivering a versioned API, agent-facing tools, and a robust, auditable execution environment for scientific software in a flexible work setup across Europe.

Qualifications

• At least five years of platform, infrastructure, or backend engineering experience.
• Experience with reproducible builds, dependency pinning, artifacts hashing, and software supply chain integrity.
• Strong Python skills; familiarity with R and scientific computing toolchains.

Responsibilities

• Make the scientific compute core reproducible through pinned toolchains and content-addressed images.
• Build the agent harness with routing, policy controls, sandboxing, usage metering, and provenance records.
• Provide a versioned API and agent-facing tools for re-running results outside the platform.
• Operate CI evaluations for conformance, re-execution, and regression; manage results by version.
• Own release engineering and operations: CI/CD, signed releases, and installation qualification.
• Implement security controls supporting multi-tenant isolation, secrets, and authorization.

Skills

Python

R

Containers

CI/CD

Kubernetes

APIs

Tools

Terraform

Docker

Git

Job description

Role overview

Design and build the platform infrastructure for AI agents to run scientific modeling workflows under controlled policies. You will make computations reproducible and verifiable, build the services agents use to access scientific tools, and own the engineering needed to release and operate the system across different deployment environments.

Responsibilities

• Make the scientific compute core reproducible through pinned R, Python, and solver toolchains, content-addressed container images, software bills of materials, and re-execution checks.
• Build the agent harness, including tool routing, policy controls, per-tenant sandboxing without network egress, usage metering, and signed provenance records for results.
• Provide a versioned API and agent-facing tools with a shared contract, plus packages that let users re-run results outside the platform.
• Operate CI evaluations for conformance, re-execution, and regression, retaining results by procedure version.
• Own release engineering and operations: CI/CD, signed releases, installation qualification, environment management, monitoring, incident response, and deployment procedures.
• Implement technical controls supporting information security and AI management standards.

Requirements

• At least five years of platform, infrastructure, or backend engineering, including senior ownership of a production system.
• Practical experience with reproducible builds and execution, dependency pinning, artifact hashing, immutable versions, and software supply chain integrity.
• Strong Python skills and comfort with R and scientific computing toolchains.
• Experience with containers, infrastructure as code, production CI/CD, Kubernetes or a similar orchestrator, observability, and release management.
• Experience designing versioned APIs used by external consumers, and with workflow or DAG runtimes and AI agent tool-calling systems.
• Security engineering experience with multi-tenant isolation, secrets, and authorization; familiarity with GxP or equivalent regulated software, or willingness to learn it.

Nice to have

• Experience with hermetic build systems.

Benefits and work setup

Flexible hours, a home office policy, and regular team gatherings across Europe. The role offers ownership of a technically significant platform problem and the opportunity to shape infrastructure for certified scientific software.