The AI Application Architect will design, implement, and scale AI-driven applications across EUNICE. This role bridges business needs with technical solutions, ensuring that AI tools are effectively integrated into operations, HR, finance, and product workflows. The Architect provides the framework, governance, and technical leadership required for successful AI adoption—supporting both efficiency and innovation across the enterprise.

Key Responsibilities
Architecture & Design
Define and maintain the AI application architecture aligned with enterprise IT standards.
Select and integrate AI tools and frameworks (chatbots, automation engines, predictive analytics, data pipelines).
Design modular, scalable, secure AI applications that support long-term growth.

Implementation & Integration
Lead end-to-end design, development, and deployment of AI applications with business and IT teams.
Build APIs and integration layers connecting AI solutions with HR, finance, operations, and product systems.
Drive proof-of-concepts and pilot projects, transitioning them into production-ready solutions.

Governance & Best Practices
Establish AI governance frameworks covering compliance, data privacy, and ethical use.
Define coding standards, documentation requirements, and architectural principles.
Provide technical mentorship and enable knowledge transfer across teams.

Collaboration & Alignment
Work with HR, Operations, and Business Units to translate functional needs into AI-enabled solutions.
Partner with stakeholders to streamline workflows, automate repetitive tasks, and enhance decision-making.
Serve as a technical advisor on AI opportunities, feasibility, and impact.

Qualifications

Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
5+ years of experience in application architecture, preferably with AI/ML systems.
Strong background in cloud platforms (Azure, AWS, GCP) and AI/ML frameworks (TensorFlow, PyTorch, LangChain, OpenAI API, etc.).
Experience with enterprise integration (APIs, middleware, event-driven architectures).
Knowledge of data engineering, databases, and MLOps practices.
Familiarity with compliance, data privacy regulations, and ethical governance in AI.
Excellent communication skills and ability to work with both technical and non-technical teams.

Key Competencies
Strategic mindset with hands-on implementation ability.
Strong problem-solving skills and systems-thinking approach.
Ability to translate business requirements into scalable AI solutions.
Continuous learner, staying current with emerging AI technologies and practices.
Collaborative leadership and strong stakeholder engagement capabilities.