Accountabilities:

Agent Platform Strategy and Architecture: Own the strategy, architecture, development, and operation of the internal Agent Platform, enabling teams to deploy, run, evaluate, govern, and continuously improve AI agents and agentic workflows.
Design and maintain scalable, reliable infrastructure that supports the lifecycle of AI agents and automated workflows across multiple teams and business use cases.
Architect AI infrastructure components, including Large Language Model (LLM) and Model Context Protocol (MCP) gateways, with appropriate guardrails, fallback mechanisms, usage monitoring, cost controls, and model rollout policies.
Design, build, and operate intelligent agents and workflows that automate engineering processes from issue identification to production deployment, as well as business processes from initial triggers to measurable outcomes.
Developer Infrastructure and Tooling: Lead the development and continuous improvement of developer infrastructure, including source control systems, Continuous Integration (CI), and internal engineering tools.
Build and maintain developer tools that simplify workflows, reduce operational friction, and improve productivity across the organization.
Cross-Functional Collaboration: Partner with stakeholders across Engineering, Product, Sales, and General and Administrative (G&A) teams to identify high-impact opportunities for AI adoption and workflow automation.
Translate complex and sometimes ambiguous business and technical requirements into scalable, practical solutions that are widely adopted.
Establish best practices for agent development and agentic tooling, creating enablement resources, documentation, and standardized workflows that encourage adoption across teams.
Performance Measurement and Optimization: Establish measurement frameworks and feedback loops to track platform usage, reliability, operational efficiency, and infrastructure costs.
Analyze quantitative metrics and qualitative feedback to evaluate productivity improvements, guide prioritization, and inform the platform roadmap.
Identify opportunities to improve the developer experience, reduce operational overhead, and strengthen platform performance.
Product and Open-Source Contributions: Act as an internal early adopter of the platform, using real organizational workflows to validate capabilities and identify product improvements.
Translate internal implementation insights into validated requirements and feedback for cloud products, SDKs, and agent development frameworks.
Collaborate with product leadership to maintain a continuous feedback loop between internal platform usage and external product development.
Contribute to the development of open-source reference architectures that other customer platform teams can adopt.
Technical Leadership and Mentorship: Mentor and support engineers, raising standards in technical design, software quality, execution, and effective use of AI-powered development tools.
Provide technical guidance and promote knowledge sharing across engineering teams.
Participate in the on-call rotation while proactively identifying improvements that reduce operational burden and enhance the internal user experience.

Requirements

Strong software engineering fundamentals, with a proven track record of leading the design, development, deployment, and operation of production-quality internal platforms or developer tools.
Experience architecting and scaling workflow automation systems or internal tools used across teams or organizations, including CLI tools, services, GitHub automation, and cloud automation solutions.
Hands-on experience with modern AI-powered coding and agentic development tools such as Codex and Claude Code, including establishing best practices and driving adoption across engineering teams.
Proficiency in at least one programming language commonly used in modern software engineering environments, such as Go, TypeScript/JavaScript, Python, or Java.
Strong understanding of software architecture, distributed systems, platform engineering, and the technical considerations involved in building reliable and scalable internal infrastructure.

How Jobgether works

We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team.

We appreciate your interest and wish you the best!

Why Apply Through Jobgether?

Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.

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