AI Solutions Engineer

Overview

Working closely with Business Integrators, data scientists, engineers, and client stakeholders, the AI Solutions Engineer leads the technical design and implementation of AI-driven solutions. The role combines deep technical expertise with leadership skills to guide teams in building high-quality AI systems that deliver real business value.

The AI Solutions Engineer also ensures that solutions are aligned with enterprise architecture standards, data governance principles, and responsible AI practices.

• Translate business use cases into scalable AI and data architectures.
• Define the technical approach for AI solutions, including model architecture, data pipelines, and system integration.
• Design end-to-end AI systems, from data ingestion and model development to deployment and monitoring.
• Ensure solutions integrate seamlessly with the client’s existing IT and data ecosystem.

• Provide technical leadership to multidisciplinary teams of AI engineers, data scientists, and developers.
• Guide teams through the full lifecycle of AI development, from prototyping to production deployment.
• Establish best practices for AI engineering, model development, and MLOps.
• Ensure code quality, system reliability, and performance.

• Work closely with Business Integrators to translate business requirements into technical solutions.
• Support the evaluation of AI opportunities by assessing technical feasibility and implementation complexity.
• Communicate technical architecture and solution choices clearly to both technical and non-technical stakeholders.

• Implement best practices for security, privacy, and compliance in AI systems.
• Design AI solutions with responsible AI principles in mind, including transparency, fairness, and explainability.
• Ensure proper monitoring, retraining, and lifecycle management of AI models.

• Stay up to date with emerging AI technologies, frameworks, and best practices.
• Contribute to the development of reusable AI components, frameworks, and architectural standards.
• Support the evolution of the organization’s AI and data architecture strategy.

• Strong experience in AI, machine learning, or advanced data engineering roles.
• Proven experience designing and implementing production-grade AI systems.
• Deep understanding of AI/ML technologies, data platforms, and modern software architecture.
• Experience with cloud-based AI platforms and data ecosystems.
• Ability to lead technical teams and guide complex AI implementations.
• Strong communication skills and the ability to explain technical concepts clearly.

• AI and machine learning architecture
• Data platform architecture
• MLOps and model lifecycle management
• System integration and scalability
• Technical leadership
• Cross-functional collaboration