AI & Data Engineering Architect

The AI & Data Engineering Architect is a strategic and high-impact role responsible for advancing organizational AI capabilities and optimizing the data ecosystem that enables them. This position focuses on defining and implementing scalable architecture patterns, tools, governance frameworks, and processes that support AI adoption and data-driven decision-making.

This role plays a critical part in designing and evolving modern data platforms and analytics solutions, ensuring they effectively support both operational intelligence and advanced AI use cases.

The AI & Data Engineering Architect works cross-functionally with enterprise architecture, data engineering, analytics, and business stakeholders to develop and execute a cohesive AI and data strategy aligned with organizational goals., * Define and execute AI & data strategy in partnership with architecture, data, and business leaders.

• Develop and maintain a multi-year roadmap covering capabilities, tools, and infrastructure.
• Design scalable AI solutions (e.g., GenAI, RAG, agent-based systems, LLM orchestration).
• Evaluate emerging AI technologies and their impact on development and productivity.
• Lead Proof of Concepts to test new approaches and transition successful solutions to production.
• Define and evolve scalable, cloud-based data platforms for analytics and AI.
• Architect ETL/ELT pipelines and data models for real-time and historical use cases.
• Establish governance standards for data quality, security, and responsible AI usage.
• Enable BI and self-service analytics with clean, governed data.
• Optimize systems for performance, scalability, and reliability.

• Hands-on experience designing and implementing AI/ML solutions, particularly with Generative AI and LLM-based applications.
• Strong background in data architecture, data engineering, or related fields, with expertise in data modeling, warehousing, and distributed systems.
• Proficiency in cloud platforms (e.g., AWS, Azure, or Google Cloud Platform), along with strong programming and query skills (e.g., Python, SQL).
• Proven ability to work across technical and business teams to translate requirements into scalable data and AI solutions.
• Familiarity with leading AI platforms, tools, and vendors, including emerging startups and LLM providers.
• Experience with advanced AI architectures, automation in the software development lifecycle, and AI-driven testing approaches.
• Experience with modern data platforms (e.g., Snowflake, Databricks) and BI tools (e.g., Power BI, Tableau, Qlik).
• Awareness of trends such as natural language-driven development, agentic workflows, and context-sharing protocols.