McCabe & Barton
Principal Enterprise Architect
London
£180k – £210k/yr
Posted about 22 hours ago
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Leading investment management firm is seeking a Senior ED-Junior MD level Enterprise AI Architect
Leading investment management firm is seeking a Senior ED-Junior MD level Enterprise AI Architect, who will be at the forefront of firm-wide AI activation, as part of the Enterprise Architecture team, to define, govern, and accelerate AI adoption. You will translate ambitious enterprise strategy into concrete architectural blueprints, ensuring that AI initiatives are coherent, scalable, secure, and aligned with fiduciary obligations.
This is a rare opportunity to shape the architectural foundations of an AI-forward firm from the ground up.
Please note that due to the nature of our client's business, we are only able to consider applicants with current experience of global responsibility for AI within a FinTech, Investment Bank or (ideally) another Investment Management firm.
Responsibilities
AI Architecture & Strategy:
- Define and maintain the firm's Enterprise AI Architecture, spanning model infrastructure, data pipelines, orchestration layers, integration patterns, and governance controls.
- Develop reference architecture for agentic AI systems and multi-agent workflows, establishing standards for orchestration frameworks, tool use, and model-context protocols (MCP) across business domains.
- Develop AI reference architectures for accelerating priority investment front-to-back office use cases.
- Drive integration of AI capabilities with core data platform and content platform, leveraging retrieval-augmented generation (RAG), MCP, etc. to unlock the firm's proprietary data assets.
Governance & Risk:
- Design and operationalize the AI governance framework, covering model risk management, explainability standards, bias monitoring, data lineage, and regulatory compliance (existing and emerging AI-specific regulation).
- Establish, evolve model evaluation and selection criteria for frontier and open-weight models, balancing capability, performance, cost, latency, etc.
- Partner with Legal, Compliance, and Risk to embed AI risk controls into architecture review processes.
- Define data privacy and security patterns for AI workloads, including prompt injection defenses, PII handling, and sovereign data requirements.
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Enterprise Alignment & Stakeholder Leadership:
- Translate business strategies from investment management, distribution, finance, and operations into AI architecture requirements and roadmaps.
- Guide Architecture Review Board (ARB) evaluations for AI-related proposals, ensuring alignment with enterprise standards, principles, and strategic direction.
- Produce executive-grade artefacts — technology radars, strategic assessments, vendor evaluations, and architectural decision records (ADRs.
- Serve as an AI thought leader and trusted advisor, building AI literacy and architectural confidence across technology and business leadership.
Technology Scanning & Innovation:
- Operate a continuous technology scanning practice, monitoring frontier AI developments (foundation models, agentic frameworks, AI infrastructure) and distilling insights for senior leadership.
- Evaluate and pilot emerging AI capabilities in a structured proof-of-concept framework, with clear criteria for progression from exploration to production.
- Maintain relationships with leading AI vendors, cloud hyperscalers, research institutions, and peer firms to benchmark capability and strategy.
Team, Collaboration & Community:
- Mentor and coach architects and engineers on AI design patterns, responsible AI practices, and architectural thinking.
- Contribute to the development of the Enterprise Architecture practice, including standards, templates, and capability-building programs.
- Represent the firm in external architecture and AI forums, industry working groups, and partner communities.
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Required:
- Bachelor’s degree in computer science, engineering, mathematics, statistics or related fields, 10+ years in technology architecture roles, with at least 3–5 years focused on AI/ML architecture in large, complex enterprise environments.
- Deep, hands-on command of the modern AI stack: LLM APIs and fine-tuning, vector databases, RAG architectures, embedding pipelines, prompt engineering, and agent orchestration frameworks (LangChain, AutoGen, or equivalents).
- Practical exposure to agentic AI architecture, multi-agent coordination, and Model Context Protocol (MCP) or similar tool-use frameworks.
- Proven experience with enterprise data platforms (Snowflake, Databricks, or comparable) and integrating AI capabilities on top of them.
- Strong understanding of cloud-native architecture on AWS, including relevant AI/ML services, e.g. Bedrock, etc.
- Demonstrated ability to produce high-quality architecture artefacts — reference architectures, technology radars, ADRs, capability assessments.
- Familiarity with enterprise architecture frameworks such as TOGAF, and experience operating within Architecture Review Boards.
- Excellent communication skills: the ability to synthesize complex technical topics into clear, actionable narratives for non-technical stakeholders.
- Experience in financial services — asset management, investment banking, or fintech — with an appreciation of investment workflows, data governance, and regulatory obligations.
Preferred:
- Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field, with a strong focus or specialization in AI.
- Knowledge of AI governance frameworks, model risk management guidelines, and emerging AI regulations.
- Familiarity with emerging AI-adjacent technologies: quantum computing implications for AI, blockchain/DLT, etc.
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Skills
Enterprise AI Architecture
LLM APIs
RAG Architectures
Agentic AI
Vector Databases
Prompt Engineering
AWS Bedrock
Snowflake
Databricks
TOGAF
Model Risk Management
AI Governance
Multi-agent Workflows
Model Context Protocol
Financial Services Architecture
Stakeholder Leadership
Location