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

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.