Join WAICE

Responsibilities

• Design and develop modern web applications.
• Integrate APIs, databases, cloud services and business tools.
• Contribute to interfaces for copilots, AI agents or internal platforms.
• Work with business, data, AI and infrastructure teams.
• Improve code quality, maintainability and documentation.

Skills

• TypeScript, React, Next.js or equivalent frameworks.
• REST APIs, system integrations and databases.
• Cloud, application security and CI/CD basics.
• Product mindset, interface quality and business understanding.
• Ability to work in client-facing environments.

Responsibilities

• Frame AI use cases with business and IT teams.
• Design prototypes, assistants, copilots or AI agents.
• Contribute to RAG, LLM, agent and knowledge layer architectures.
• Evaluate answer quality, hallucinations, risks and limitations.
• Support documentation, standards and AI adoption.

Skills

• Understanding of LLMs, RAG, embeddings, agents and AI APIs.
• Python, notebooks, model evaluation or AI frameworks.
• Data, governance, security and confidentiality mindset.
• Ability to explain AI concepts to non-technical teams.
• Discipline around evaluation, limitations and risk.

Responsibilities

• Structure scope, planning, deliverables and project governance.
• Coordinate client teams, technical teams, business stakeholders and partners.
• Track risks, dependencies, priorities and decisions.
• Organize workshops, steering committees, demos and decision points.
• Ensure continuity between framing, integration, adoption and run.

Skills

• IT project management, agile or hybrid delivery.
• Understanding of cloud, data, software or AI environments.
• Ability to communicate with business, IT, data and leadership teams.
• Documentation discipline, follow-up and risk management.
• Delivery, client and operational result mindset.

Responsibilities

• Design usable data and AI architectures.
• Connect data sources, APIs, cloud platforms and business tools.
• Contribute to ingestion, transformation, indexing or vectorization pipelines.
• Build the foundations for assistants, agents and AI use cases.
• Support data governance, traceability and quality.

Skills

• Data engineering, SQL, Python, APIs and cloud architecture.
• Understanding of lakehouse, data warehouse, vector database or RAG concepts.
• Security, access rights and data exposure awareness.
• Ability to work with AI, IT and business teams.
• Maintainability and industrialization mindset.

Responsibilities

• Implement monitoring, versioning, testing and deployment practices.
• Track the quality of models, prompts, agents and generated answers.
• Monitor costs, incidents, usage and drift.
• Create runbooks, dashboards, procedures and quality indicators.
• Work with DevOps, data, security and business teams.

Skills

• CI/CD, observability, monitoring, cloud and automation.
• Understanding of ML models, LLMs, RAG, agents and APIs.
• Testing, evaluation, hallucination, drift and service quality concepts.
• Production support and operational continuity mindset.
• Discipline around security, costs and documentation.

Responsibilities

• Gather and formalize business needs.
• Translate use cases into user stories, processes, rules and acceptance criteria.
• Support framing, prioritization and adoption workshops.
• Help users adopt AI solutions.
• Track value delivered, pain points, user feedback and evolutions.

Skills

• Business analysis, requirement formalization and workshop facilitation.
• Understanding of information systems, data and AI.
• Ability to work with business, IT, data, security and leadership teams.
• Product mindset, user adoption and continuous improvement.
• Documentation discipline and synthesis skills.