Job Description

Roles & Responsibilities

Lead the architectural design and delivery of AI-driven solutions that translate business problems into production-ready systems. You will define solution blueprints, select appropriate models and tooling, and drive integrations with cloud services and enterprise applications to ensure reliability and compliance. Partner with product managers, engineering teams and data scientists to prioritise work, de-risk delivery and accelerate time to value, while improving operational observability and model lifecycle management. Influence technical standards, review designs and mentor engineers to raise implementation quality across multiple initiatives. Key Responsibilities Own end-to-end solution architecture for AI initiatives, from requirements through to deployment and operational handover. Define model integration patterns, data pipelines, and inference infrastructure tuned for performance and cost. Drive cross-functional design workshops to align stakeholders on scope, constraints, and success criteria. Produce architecture artefacts including diagrams, interfaces, and non-functional requirement specifications. Evaluate and recommend cloud services, frameworks, and tooling to meet security, compliance and scalability needs. Establish practices for model governance, monitoring, retraining triggers and observability. Conduct technical reviews, code and design reviews, and mentor engineering and data science teams. Support proof-of-concept work to validate approaches and rapidly iterate on promising directions.

Desired Candidate Profile

• proven experience designing and delivering production AI or ML solutions in enterprise environments.
• strong knowledge of cloud platforms and experience choosing services for compute, storage, and deployment of ML models.
• demonstrable skills in system design, APIs, data engineering patterns and scalable inference architectures.
• experience working with stakeholders across product, security, legal and operations teams in regulated industries.
• excellent communication, modelling and documentation skills with a track record of influencing technical decisions.
• hands-on experience with MLOps, model monitoring tools, feature stores or model registries.
• practical exposure to large language models, prompt engineering or vector search technologies.
• relevant certifications in cloud platforms or architecture frameworks.