AI Engineer

About Atira

Atira develops advanced AI-powered commercial solutions for industrial companies, dramatically enhancing the productivity of sales engineers. Their intelligent agents manage complex tasks such as analyzing requests for quotations, coordinating communications among multiple stakeholders and systems, and generating essential outputs like documentation, inputs for production systems, and configuration recommendations integrating CRM and ERP systems.

Based in Munich, Atira is rapidly growing and seeks capable AI engineers to join the team. The company emphasizes engineers as the cornerstone of all that is delivered to customers.

Role Overview

In this role, you will develop cutting-edge AI frameworks including agent runtimes, evaluation mechanisms, and verification processes suited for complex industrial engineering scenarios. You will enable agents to accurately interpret multi-party processes covering inbound data, product configuration, proactive data gathering, complex technical writing, and integration with operational systems.

The work demands high precision, as misinterpretation of specifications can incur significant financial losses for manufacturers.

Key Responsibilities

• Design and improve the core execution frameworks and orchestration layers that facilitate agent reasoning throughout the sales engineering lifecycle, including SDKs and harnesses.
• Enhance the company's proprietary AI capabilities by managing quality control, customizing evaluation tools, refining ontologies that capture process knowledge, and integrating deterministic safeguards with adaptable frameworks for enterprise-level agent effectiveness.
• Scale AI infrastructure to handle massive volumes of diverse industrial data inputs such as long documents, pricing sheets, technical drawings, CAD files, ERP data, and configuration rules, ensuring reliability, no silent errors, and performance under millions of concurrent AI model requests.
• Develop internal AI tools supporting front-end developers, platform engineers, and GTM teams to improve the system’s contextual data layers, model usage, and skill integrations.
• Collaborate closely with front-end developers to resolve issues tracing back to runtime, pipelines, or infrastructure, ensuring seamless platform functionality.

Candidate Profile

• Strong foundation in software engineering with proficiency in Python, TypeScript, or equivalent, plus practical experience building and deploying AI agents, runtimes, evaluation frameworks, sandbox environments, and related tools.
• Extensive experience leveraging AI-assisted development tools such as Claude Code, Cursor, or Codex, demonstrating advanced capabilities in building efficient coding workflows augmented by AI.
• Well-versed in scalable system architecture capable of handling millions of calls daily, with the capacity to assess and clearly communicate the advantages and disadvantages of various technical solutions.
• Demonstrated ownership and initiative, evident through entrepreneurial ventures, significant open-source contributions, scalable side projects, or roles requiring autonomous technical outcome delivery.
• A relevant university degree in Computer Science, Information Systems, AI, or a comparable discipline from a reputable institution.
• Fluent English communication skills; German proficiency is not mandatory.

Benefits and Culture

• Opportunity for substantial ownership as an early team member influencing product direction, architecture, and direct client collaboration.
• Work alongside experienced peers with a proven track record of developing AI systems for industrial applications at scale.
• Engage in impactful projects whose deliverables influence complex product sales and configuration processes across Europe and the US.
• Competitive salary combined with equity and additional benefits such as Wellpass, JobRad, and company-sponsored social events.
• A collaborative and high-performing workplace culture grounded in modesty and integrity.

Indicators of Early Success

• Successfully delivering enhancements to the agent runtime, AI pipelines, and supporting systems currently deployed in multiple client environments.
• Assuming ownership over key components of the AI stack, earning team trust regarding strategic decisions and future development.
• Ability to troubleshoot and resolve production issues spanning agent behaviors to infrastructure layers.
• Enhancement of internal AI tooling to significantly boost engineering efficiency and quality.
• Making design choices that endure real-world operational load while articulating associated trade-offs and improvement plans.