Systems Integration Advisor

Key Responsibilities

·                Lead the evaluation and strategic assessment of emerging AI technologies, platforms, and vendor solutions, advising on technical and ethical feasibility.

·                Design and guide the development of AI capabilities and innovation pilots, translating business goals into AI-enabled solutions.

·                Define architectural blueprints for integrating AI technologies into IT systems and product platforms, ensuring security, scalability, and alignment with enterprise standards.

·                Develop frameworks for responsible AI adoption including model evaluation, explainability, privacy, compliance (e.g., EU AI Act), and ethical use.

·                Partner with product and platform teams to align AI innovations with enterprise technology strategy and business outcomes.

·                Drive initiatives for AI prototyping, proof-of-concepts (PoCs), and production readiness assessments.

·                Monitor vendor roadmaps and contribute to the strategy for selecting and onboarding external AI capabilities.

·                Act as a center of excellence for AI within the IT organization, driving awareness, knowledge sharing, and standardization.

·                Collaborate with enterprise architects and platform leads to integrate AI tools into data infrastructure, software architecture, and cloud environments.

·                Perform technical due diligence on third-party AI services and models, ensuring fit-for-purpose and cost-effective solutions.

·                Continuously improve internal innovation processes and methodologies to increase the speed and quality of AI-driven transformation.

Knowledge and Attributes

·                Deep knowledge of modern AI paradigms including generative AI (e.g., LLMs), machine learning infrastructure, AI model lifecycle, and MLOps.

·                Strong understanding of AI model risks and evaluation techniques, with experience applying responsible AI principles in enterprise environments.

·                Excellent ability to assess multi-vendor and open-source AI offerings for enterprise adoption.

·                Advanced knowledge of reference AI architectures, integration models, and cloud-based AI toolchains.

·                Ability to translate business needs into technical AI strategies, architectures, and implementation roadmaps.

·                Excellent communication and stakeholder management skills across technical, executive, and vendor audiences.

·                Strategic thinker with the ability to rapidly assess the value and risks of new AI technologies and innovation trends.

Academic Qualifications and Certifications

·                Bachelor’s degree or equivalent in Computer Science, Artificial Intelligence, Data Science, or a related field.

·                Advanced degrees (MSc/PhD) in AI/ML fields preferred.

·                TOGAF, COBIT, or related enterprise architecture certifications are beneficial.

·                Certifications in machine learning or cloud-based AI platforms (e.g., AWS Certified Machine Learning Specialty, Google Cloud AI Engineer) are advantageous.

Required Experience

·                Extensive experience in leading enterprise AI innovation and architecture initiatives.

·                Proven track record of evaluating, piloting, and operationalizing AI solutions in enterprise environments.

·                Experience working across multiple industries and large-scale IT organizations.

·                Hands-on experience in AI/ML development, integration, and lifecycle management.

·                Familiarity with regulations governing AI use, such as the EU AI Act, and experience in operationalizing compliance measures.