Senior GenAI Engineer, Inteligent Platforms

HelloFresh's GenAI Enablement Squad is a cross-functional team focused on building AI-powered solutions to solve high-impact problems across the company, supporting 6+ million customers and complex supply chain operations.

• Experience building and shipping production AI applications using modern LLMs, including agents, RAG, workflow automation, structured generation, and evaluation.
• Strong software engineering skills designing and operating production systems.
• Experience working directly with customers or business stakeholders to translate ambiguous problems into practical software solutions.
• Product mindset focused on delivering measurable user and business impact.
• Ability to rapidly prototype ideas while balancing scalability, reliability, and maintainability.
• Experience with managed AI platforms such as AWS Bedrock, OpenAI, Anthropic, or Google Vertex AI.
• Strong programming skills in Python or another modern programming language.
• Excellent communication and collaboration skills across technical and non-technical audiences.
• Curiosity, pragmatism, and a bias toward action.

Nice to have:

• Experience building internal developer tools or productivity platforms.
• Experience evaluating AI systems for quality, latency, and cost.
• Familiarity with agent frameworks, MCP, tool calling, or workflow orchestration.
• Experience deploying AI solutions in enterprise environments.
• Experience measuring adoption and business impact of AI products.

• Partner with engineering and business teams to identify AI opportunities and understand workflows.
• Embed with teams to prototype, gather feedback, and iterate AI solutions.
• Design, build, deploy, and operate production-grade AI applications such as agents, workflow automations, and internal tools.
• Build role-specific AI assistants distributed via internal plugin marketplace.
• Manage full AI solution lifecycle from discovery to continuous improvement.
• Ensure solutions are reliable, observable, secure, and cost-effective.
• Collaborate with platform engineers to leverage and improve shared AI infrastructure.
• Share learnings and reusable patterns to accelerate AI initiatives company-wide.
• Mentor teammates through collaboration, code reviews, and technical discussions.