AI Solution Architect

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

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.