Information and Organisation Professionals

Job Description

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Design and architect end‑to‑end AI solutions aligned to business objectives, translating complex requirements into scalable, secure, and high‑performing architectures.

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• Collaborate with business stakeholders, product owners, and engineering teams to identify AI use cases and define solution roadmaps and reference architectures.
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• Lead the selection, integration, and optimization of AI/ML platforms, data platforms, cloud services, and tooling across the solution stack.
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• Define data, model, and integration architectures, ensuring robustness across data ingestion, model development, deployment, monitoring, and lifecycle management.
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• Provide architectural governance and technical leadership, ensuring solutions adhere to enterprise standards, security, privacy, and responsible AI principles.
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• Guide development teams on best practices for AI/ML engineering, MLOps, and scalable deployment patterns in cloud and hybrid environments.
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• Conduct solution reviews, proofs of concept, and technical assessments to validate feasibility, performance, and cost‑effectiveness of AI solutions.
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• Act as a trusted technical advisor to clients and internal teams, supporting pre‑sales activities, solution proposals, and executive‑level technical discussions.
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• Bachelor’s degree in Computer Science, Engineering, Data Science, or a related discipline; a Master’s degree or relevant AI/Cloud certifications is preferred.
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• 8+ years of overall IT experience, with at least 4–5 years in solution architecture, AI/ML, data platforms, or advanced analytics roles.
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• Strong hands‑on knowledge of AI/ML concepts, including machine learning, deep learning, generative AI, NLP, and model lifecycle management.
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• Proven experience architecting solutions on cloud platforms (Azure, AWS, or GCP), including AI services, data storage, compute, and integration patterns.
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• Solid understanding of data architectures, including data lakes, data warehouses, streaming, and ETL/ELT pipelines.
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• Experience with MLOps practices, model deployment, monitoring, CI/CD, and scalable production environments.
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• Strong stakeholder management and communication skills, with the ability to explain complex AI solutions to technical and non‑technical audiences.
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• Experience in defining enterprise‑grade architectures with a focus on security, privacy, compliance, and responsible/ethical AI practices.
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