Help design, build and continuously improve the clients online platform.
Research, suggest and implement new technology solutions following best practices/standards.
Take responsibility for the resiliency and availability of different products.
Be a productive member of the team.

Requirements

AI Solution Architect with several years of production software engineering experience in Python, TypeScript, Java, or comparable technology stacks.
Hands-on experience building LLM and agent-based solutions on at least one major AI platform, combined with strong software engineering, integration, and production delivery capabilities.
Design and develop AI solutions using LLMs and agentic architectures.
Build production-ready solutions using Python, TypeScript, Java, or comparable technologies.
Work hands-on with major AI platforms such as Anthropic, OpenAI, Google, or Microsoft.
Apply agentic patterns including tool use, retrieval, prompting, model behaviour, evaluation, and multi-agent workflows.
Design multi-agent solutions involving orchestration, delegation, and self-reflection.
Integrate AI solutions with APIs, databases, cloud platforms, and enterprise integration environments.
Apply strong practices around debugging, testing, integration, and production engineering.
Collaborate with customers, users, and cross-functional technical teams in ambiguous and evolving delivery environments.
Balance rapid prototyping with scalable, maintainable, and production-quality engineering.
Experience taking agentic AI or LLM solutions from prototype through production rollout.
Experience with agent orchestration frameworks, MCP servers, and custom tool integrations.
Experience with data and retrieval pipelines, workflow automation, or model deployment.
Experience with AI evaluation, observability, troubleshooting, and release safeguards.
Vendor certification in a major AI platform such as Anthropic, OpenAI, Google, or Microsoft.

Benefits

A challenging, innovating environment.
Opportunities for learning where needed.

Compétences requises

PythonTypeScriptJavaLLM and agent-based solution developmentAgentic architecture design (multi-agent orchestration, delegation, self-reflection)Working with major AI platforms (Anthropic, OpenAI, Google, Microsoft)Agent orchestration frameworks, MCP servers, and custom tool integrationsPrompt engineering, tool use and model behavior tuningIntegration with APIs, databases, and cloud platformsData and retrieval pipeline development and workflow automationAI evaluation, observability, troubleshooting, and release safeguardsProduction engineering practices (debugging, testing, integration, production delivery)Cross-functional collaboration with customers and technical teams in ambiguous environmentsRapid prototyping and production rollout (balancing speed with maintainability)Vendor certification in a major AI platform (e.g., Anthropic/OpenAI/Google/Microsoft)

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