As an AI Engineer Co-Op, you will work alongside experienced AI architects, AI engineers, and platform engineers to design, develop, and operationalize AI solutions that support ASML's business and technology objectives.
Responsibilities
Build and enhance AI-powered applications using modern LLMs, agent frameworks, and AI orchestration capabilities.
Develop and test prompts, AI workflows, RAG solutions, and agentic AI systems.
Assist in deploying secure and governed AI solutions on enterprise-approved platforms.
Contribute to MLOps and AI governance capabilities that improve transparency, reliability, and compliance.
Support evaluation frameworks, scorecards, benchmarking, and AI quality measurement processes.
The role focuses on advanced prompt engineering, foundation model optimization, and programmatic evaluation of LLM applications for financial services and healthcare sectors.
Responsibilities
Programmatically generate, test, and evaluate prompt performance at scale using Python.
Manage foundation model behaviors (GPT-4, Llama, Claude, Mistral), context window dynamics, and tokenization mechanics.
Implement output validation libraries (Pydantic, Instructor) and guardrail systems.
Focus on edge-case mapping, deterministic output formatting, and cost optimization.
Apply advanced prompting techniques such as Few-Shot Prompting, Chain-of-Thought (CoT), ReAct, and Context Engineering.
Lead the architecture, development, deployment, and ongoing evolution of AI initiatives across a global consumer SuperApp. This is a hands-on technical leadership role focused on open-source foundation models, self-hosted infrastructure, and production AI systems.
Compensation
Compensation is accrued and payable following funding, after which it transitions to a regular salary, equity ownership, and OleX token participation in the overall OleOle group of companies.
Responsibilities
Establish overall AI architecture
Lead initiatives including AskOle
Lead real-time multilingual translation
Develop AI-powered sports history and content generation systems
Develop the sports data engine
Requirements
Nice to have: building consumer AI platforms at scale
Nice to have: multilingual and multimodal AI engineering
Lead AI consulting engagements, advising clients on AI strategy, transformation, and adoption, while driving business outcomes and managing client relationships.
Responsibilities
Act as a trusted client advisor to identify AI transformation opportunities, business pain points, and value cases.
Shape AI strategy, target operating model, governance, adoption, responsible AI, and transformation roadmaps for clients.
Translate client needs into practical AI solutions in partnership with architects, FDE, delivery, and partner teams.
Develop proposals, business cases, ROI models, proof-of-concept plans, and executive-ready client materials.
Support opportunity qualification, deal shaping, solutioning, mobilization, and transition into delivery.
We are looking for a Lead Fullstack Developer to join our Smart Data pole team of 25 people split between Lyon and Rennes. The role focuses on transforming document bases into intelligent platforms using Generative AI, LLMs, and RAG.
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