AI/ML Solution Architect

This is a high-impact architecture role focused on designing production-grade AI systems for education, assessment, and other large-scale public-sector environments. You’ll bridge discovery and delivery, translating complex stakeholder requirements into secure, scalable, and cost-conscious technical architectures. The role spans GenAI, RAG, multi-agent systems, document AI, LLM evaluation, cloud platforms, and LLMOps/MLOps. You’ll work closely with engineering teams, programme leaders, domain specialists, and institutional stakeholders across multiple countries. Your designs will need to operate reliably under demanding requirements around privacy, auditability, security, multilingual content, and sovereign hosting. This is an opportunity to shape AI systems intended for real-world deployment and measurable impact at significant scale.

• 8+ years of experience building software, data, or AI/ML systems, including recent experience as a Solution Architect, AI/ML Architect, Principal Engineer, or Staff Engineer with end-to-end architecture ownership.

• Proven experience shipping at least one production AI/ML or GenAI system at scale, such as RAG, multi-agent systems, LLM-as-judge pipelines, or classical ML/NLP solutions that successfully passed security, cost, and operational reviews.

• Demonstrated ability to create delivery-ready architecture blueprints covering services, data stores, IAM, trust boundaries, failure modes, evaluation strategies, and cost models.

• Deep experience with Azure and/or AWS, including the ability to design and defend landing zones, private-endpoint architectures, cloud security controls, and real-world infrastructure and inference costs.

• Strong Python skills, with practical understanding of classical machine learning, model evaluation, LLM APIs, embeddings, vector search, and production AI behavior.

• Hands-on experience with LLMOps/MLOps, including model and prompt versioning, evaluation harnesses, CI/CD release gates, observability, experiment tracking, and rollback strategies.

• Strong understanding of RAG over structured knowledge, hybrid search, document AI/OCR, multilingual AI, agent security, prompt-injection risks, and AI evaluation methodologies.

• Experience working with non-technical stakeholders and facilitating workshops that translate business, programme, or government requirements into technical solutions.

• Excellent written English and strong documentation skills, with the ability to create architecture materials that function as clear technical contracts for engineering teams.

• Strong problem-solving, decision-making, communication, and collaboration skills, with the ability to work independently while influencing technical direction across multidisciplinary teams.

• Preferred experience includes Azure AI Foundry, Azure OpenAI, Azure Document Intelligence, AWS Bedrock or SageMaker, GCP Vertex AI or BigQuery, MLflow, Databricks, Microsoft Fabric, Semantic Kernel or Microsoft Agent Framework, MCP integrations, constraint solvers, and exam or assessment platforms.

• Experience in education, assessment, public sector, healthcare, finance, identity, or other high-audit environments is highly valuable, as is familiarity with sovereign hosting, government data systems, multilingual or code-mixed language applications, and data-sharing requirements.

• Relevant cloud certifications, such as Azure Solutions Architect or Data Scientist and AWS Solutions Architect or Machine Learning certifications, are advantageous.

• The role requires comfort working across international time zones, with early mornings or late evenings occasionally forming part of the working rhythm.