As Applied AI Lead, you will lead the next phase of AI deployment at Dipli.

AI is already used across several teams on concrete topics such as pricing prediction, device assessment, internal tooling, and workflow automation. The goal now is to scale, structure, and industrialize these initiatives across the company.

You will work across Tech, Product, Data, Operations, and Business, with a strong focus on applied systems, execution, and impact.

This is a cross-functional role for someone who can turn AI capabilities into production-grade tools, workflows, and decision systems.

Mission:

1. Scale existing AI initiatives

Build on current use cases and help bring them to the next level in terms of performance, reliability, and adoption.

2. Drive new applied AI projects

Identify high-value opportunities and lead execution from framing to deployment across operational, product, and internal workflows.

3. Structure Dipli’s AI stack

Help define the technical foundations, standards, and best practices for applied AI across the company, including agent-based systems and MCP-oriented approaches.

4. Accelerate agents and automation

Push the use of AI agents and automation layers where they can improve speed, quality, and decision-making across teams.

5. Ensure production impact

Define success metrics, monitor performance, and continuously improve deployed systems with a strong focus on business and operational value.

Education

Master’s degree from a top engineering school or equivalent quantitative background.

Background

Strong experience in applied AI in a demanding tech environment.

Proven track record shipping real-world AI systems to production.

Experience working across model design, integration, evaluation, and deployment.

Experience with modern AI systems such as LLM-based applications, predictive models, orchestration layers, agents, and production tooling.

Experience in startup or scale-up environments is a plus.

Skills

Applied AI: strong understanding of modern ML / AI systems and their concrete deployment constraints.

Technical depth: able to engage deeply on architecture, model integration, evaluation, tooling, and system reliability.

Execution: able to move fast from idea to production in cross-functional environments.

Product sense: understands where AI creates real leverage and avoids low-value complexity.

Communication: comfortable aligning technical and non-technical stakeholders.

Languages: fluent in English.

Mindset

Ownership: takes responsibility end-to-end.

Mission-Driven: motivated by building AI systems in service of a more circular economy, and excited by the opportunity to work on concrete challenges linked to second-life electronics, reuse, and industrial impact.

Intensity: enjoys ambitious environments with high expectations.

Curiosity: interested in building at the intersection of AI, software, operations, and business.

Fit call – 30 min

Team call – 45 min

On-site interviews at Dipli – 2 sessions of 1h30