AI Solution Architect

Job description

ROLE SUMMARY: We are looking for an AI Solution Architect who can take an enterprise AI initiative from vision to execution. You will shape AI strategy with C-level stakeholders, architect cloud-native GenAI and ML platforms on Azure and Databricks, and lead cross-functional teams across data science, MLOps and product engineering to deliver them. You are equally comfortable presenting a roadmap to a board, reviewing a RAG pipeline design and coaching an engineer through a production incident.

Experience • 12+ years in data, analytics or software engineering, including 5+ years leading AI/ML platforms or products in an enterprise setting. • Proven record of taking ML and GenAI solutions into production at scale, with measurable business impact. • Hands-on delivery of at least one production LLM/RAG solution, plus hands-on experience with agentic AI patterns. • Experience leading multidisciplinary teams and presenting to C-level executives. • Bachelor's or master's degree in computer science, Data Science, Engineering, Statistics or a related field.

Cloud & MLOps: Azure (ADF, Azure ML, Synapse, Event Hubs, Key Vault), Databricks, Kubernetes, Docker, Terraform, CI/CD Data & ML: Python, SQL, PySpark, MLflow, TensorFlow, Gurobi, Dataiku, SAS, Alteryx GenAI & Agentic AI: OpenAI and other LLMs, LangChain, Milvus / vector databases, RAG, prompt engineering, memory agents, NLP, LLM evaluation BI & Visualisation: Power BI, Tableau, Spotfire, Qlik

Leadership Competencies: • AI strategy and roadmap execution • AI/ML product lifecycle management • Responsible AI and governance • Stakeholder engagement and executive communication • Training, change management and AI adoption • Cross-functional team leadership • Vendor and partner collaboration

Nice to Have • Certifications such as Azure Solutions Architect Expert, Azure AI Engineer, Databricks ML Professional or TOGAF. • Experience with AWS or GCP AI services, or multi-cloud architectures. • Industry exposure in financial services, energy, healthcare, manufacturing or the public sector. • Experience designing or delivering AI training and enablement content.

SUCCESS IN THE FIRST 12 MONTHS • An agreed AI roadmap and reference architecture adopted across key client engagements. • At least two GenAI or ML solutions in production with tracked business value. • A working Responsible AI and MLOps framework reused by delivery teams. • A high-performing, cross-functional team and a strong bench of client executive relationships.

Reports to: Chief Executive Officer / Head of AI

Requirements

AI Strategy & Leadership • Define and own AI strategy and multi-year roadmaps aligned to client and C-level business priorities, with clear value metrics (revenue, cost, risk, productivity). • Lead the full AI/ML product lifecycle: opportunity discovery, business case, architecture, build, deployment, adoption and value tracking. • Build, mentor and lead agile delivery teams spanning data scientists, ML/MLOps engineers, data engineers and product managers. • Act as a trusted advisor to executives, translating complex technical options into clear decisions on investment, risk and trade-offs.

Solution Architecture & Delivery • Architect enterprise-grade, cloud-native AI/ML and analytics platforms on Azure (ADF, Azure ML, Synapse, Event Hubs, Key Vault) and Databricks. • Design and deliver GenAI and agentic AI solutions: LLM integrations (OpenAI and others), retrieval-augmented generation (RAG), vector databases (e.g. Milvus), prompt engineering, memory-enabled agents and NLP pipelines. • Establish MLOps foundations using MLflow, Docker, Kubernetes and Terraform for reproducible training, CI/CD, monitoring and scalable model serving. • Set architecture standards, reference designs and reusable components that reduce time-to-production across engagements. • Guide data and analytics solutions end to end, from pipelines (Python, SQL, PySpark) to optimisation (Gurobi) and BI dashboards (Power BI, Tableau, Spotfire, Qlik).

Responsible AI & Governance • Define and embed Responsible AI practices: fairness, explainability, privacy, security, model risk management and human oversight. • Design governance for GenAI, including evaluation frameworks, guardrails, hallucination and prompt-injection controls, cost monitoring and audit trails. • Ensure solutions comply with relevant regulations and client policies (e.g. PDPA, GDPR, MAS FEAT principles where applicable).

Stakeholder, Change & Adoption • Engage business, IT, security and risk stakeholders to align scope, secure buy-in and manage expectations. • Lead training, change management and AI adoption programmes so solutions are used, trusted and sustained. • Manage vendor and partner relationships (cloud providers, LLM providers, platform and SI partners), including evaluation, selection and commercial input. • Produce clear executive communication: roadmaps, status reports, value realisation reviews and steering committee materials.