Enterprise AI Architect

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).
• 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

• 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.

Pay

$174,000.00 - $297,000.00 for the location of: Maryland, Colorado, Washington and remote workers
$191,000.00 - $327,000.00 for the location of: Washington, D.C.
$218,000.00 - $350,000.00 for the location of: New York, California

Schedule

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