Senior Backend Engineer – AI Platform

Description

About the Role

As a Senior Backend Engineer on the AI Platform team, you will develop high-impact platforms, autonomous agents, and tools designed specifically for our internal software developers and researchers. Our team serves as the company’s focal point and center of excellence for AI innovation - acting as "developers for developers" across both internet-connected and air-gapped environments to automate engineering workflows and accelerate delivery. We partner closely with R&D teams to understand their workflows, gather requirements, and architect the right technical solutions.

In this role, you will be a pioneer of modern AI-first engineering workflows. Rather than manually writing routine boilerplate, you will develop tools while utilizing cutting-edge coding harnesses as your primary force multiplier. You will navigate the broader ecosystem, including plugin marketplaces, mods, and custom tool extensions while directing system architecture, evaluating technical trade-offs, and ensuring production-grade code quality. You will tackle engineering challenges ranging from building autonomous CI/CD agents to evaluating, porting, and deploying the latest open-source AI tooling into secured networks.

Key Responsibilities

• Architect & Build Developer Platforms: Design, develop, implement, and scale backend services and developer platforms that integrate LLMs and autonomous agents directly into everyday engineering workflows across connected and isolated environments.

• Build Autonomous Coding & Review Agents: Develop event-driven automations (e.g., triggering bots via GitLab/GitHub issues and merge requests) powered by Claude Code, Codex or any other agent harnesses to handle automated code reviews and end-to-end feature implementations.

• Evaluate & Port Open-Source AI Tooling: Actively research emerging open-source AI tooling including solutions (e.g. Onyx and Open WebUI) for enterprise RAG and document/data-source chat, open-source OCR engines (e.g. Surya, Marker, PaddleOCR) for document parsing, and observability platforms (e.g. Langfuse and Opik). Conduct trade-off analyses, lead technical proofs-of-concept, and adapt these tools for air-gapped deployment.

• Bridge Connected & Air-Gapped Environments: Solve distribution, packaging, dependency management, and containerization challenges required to take modern tools from internet-connected environments and deploy them reliably in air-gapped networks.

• Act as the AI Engineering Focal Point: Serve as the central knowledge authority for internal R&D teams, advising them on adopting agent harnesses, plugins, and custom extensions while establishing company-wide best practices for architecture, edge-case handling, and code verification. 

Requirements

Requirements

• Backend Expertise: 5+ years of professional backend development experience designing, building, and operating robust, scalable systems and architectures.

• Core Language: Strong expertise in Python and modern backend architecture.

• Core Tooling & OS: Strong proficiency in Linux environments and Git version control workflows.

• LLM Harnesses & Ecosystem: Hands-on experience developing with modern AI coding harnesses and agentic tools (e.g. Claude Code, Codex), along with familiarity with their ecosystems (plugin marketplaces, mods, extensions) and SDK integrations (e.g. Claude SDK).

• AI-Accelerated Engineering Mindset: An intuitive ability to drive LLMs to produce production-grade code, combined with the architectural depth to audit, guide, and correct model outputs.

• Containerization: Solid working experience with Docker and Docker Compose.

• Collaborative & Product-Oriented: Strong team player with high empathy for internal developer pain points and the ability to translate ambiguous requirements into clean, resilient platform solutions.

Advantages

• B.Sc. in Computer Science, Software Engineering, or a closely related technical field.

• Experience building and maintaining CI/CD pipelines (GitLab CI, GitHub Actions) and configuration automation (e.g., Ansible).

• Familiarity with AI orchestration frameworks (e.g. LangChain, LlamaIndex).

• Experience in packaging, containerizing, or managing infrastructure in on-premises environments.

• Hands-on experience with open-source RAG, vector databases, or enterprise search platforms.

• Experience with LLM observability, tracing, and evaluation tools (e.g. Langfuse).