Senior AI Architect

Role & Responsibilities: 

Key Responsibilities

• Architecture & Technical Standards:

  • Define the architecture for agentic systems and AI applications, covering patterns, components, integration and scalability, aligned with enterprise standards.
  •  Set standards for model selection across LLMs and traditional ML, together with frameworks and tooling, balancing performance, cost, security and maintainability.
  • Design LLM and ML pipelines and agent-orchestration patterns, defining how agents are built, evaluated, versioned and monitored.
  • Architect AI workloads on cloud platforms and define how they integrate with enterprise data and systems.
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• Hands-On Delivery:

  • Personally prototype and build core components of priority AI applications rather than only designing them.
  • Pair with engineers to accelerate delivery and de-risk the hardest technical problems.
  • Review solution designs and drive adoption of architectural standards across the AI squad.
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• Responsible AI & Governance:

  • Embed Responsible AI, model-risk and compliance guardrails into the architecture.
  • Partner with governance, risk and security teams to ensure AI systems meet institutional control requirements.
  • Apply model-risk controls, ethical AI principles and recognised compliance frameworks to production systems.
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• People & Leadership Responsibilities:

  • Guide AI engineers on design and implementation, setting the technical bar for the team.
  • Act as the senior technical reference point for AI across the organisation.
  • Build the architectural discipline and engineering practices of a newly formed AI function.
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• Internal & External Interfaces:

  • Works with data architecture and data engineering teams to ensure agents consume trusted, governed data.
  • Collaborates with product and business stakeholders to translate requirements into technical design.
  • Engages governance, risk, compliance and information-security functions on AI controls.
  • Interfaces with cloud providers, model vendors and technology partners on platform decisions.

Requirements

• Education & Qualifications:

  • Advanced degree in Computer Science, Engineering, Data Science or AI/ML.
  • Credentials in AI governance or cloud/AI architecture preferred.
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• Experience Requirements:

  • 8 to 12+ years in AI/ML engineering or architecture, including agentic and LLM systems.
  • Recent hands-on engineering experience, having personally built and deployed LLM and agentic applications rather than only overseen them.
  • Track record architecting production-grade AI and agentic systems with measurable impact
  • Experience in financial services, sovereign enterprises or investment organisations preferred
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• Technical Expertise:

  • Agentic systems architecture and orchestration using frameworks such as LangChain and LangGraph
  • Strong command of LLMs, ML pipelines and production engineering with tools such as PyTorch, TensorFlow and MLflow, plus traditional ML such as LightGBM and XGBoost
  • Software engineering fundamentals including API design, testing, version control, CI/CD and production-quality code
  •  AI governance and Responsible AI, including model-risk controls and compliance frameworks
  • Cloud architecture for AI workloads and integration with enterprise data and systems