Enterprise AI Architect

We are at an inflection point. Artificial intelligence is reshaping every dimension of asset management — from investment research and portfolio management to client engagement, and operational efficiency.

As Enterprise AI Architect, you will be at the forefront of firm-wide AI activation, as part of the Enterprise Architecture team and working directly with the Chief Architect, to define, govern, and accelerate AI adoption across a complex, global institution. You will translate ambitious enterprise strategy into concrete architectural blueprints, ensuring that AI initiatives are coherent, scalable, secure, and aligned with our fiduciary obligations.

This is a rare opportunity to shape the architectural foundations of an AI-forward firm from the ground up.
 

Why This Role Matters:

The Enterprise Architecture team serves as the connective tissue between technology capability and business strategy execution. With AI emerging as the defining technology of this era, the team requires a dedicated architect who brings both the depth to evaluate frontier AI systems and the breadth to integrate them into our global enterprise technology and data estate.
 

You will operate across three horizons simultaneously:

• Now — Guiding and accelerating in-flight AI initiatives, establishing guardrails and patterns for responsible deployment.

• Next — Designing the target-state AI architecture that integrates with our cloud enabled app and data stack, and our evolving digital workplace solutions.

• Beyond — Scanning the frontier, evaluating emerging capabilities (agentic AI, MCP-based orchestration, multimodal reasoning, quantum-AI convergence), and identifying strategic bets for the 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.
   

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 artifacts — 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.
   

Qualifications
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 artifacts — 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.
   

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.

• Experience in financial services — asset management, investment banking, or fintech — with an understanding of investment workflows, data governance, and regulatory obligations.

• 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.

Work Flexibility
This role is eligible for hybrid work, with up to three days per week from home.