EP Wealth Advisors (EPWA) is a wealth management advisory firm with over $44.1 billion AUM as of March 31, 2026, serving predominately high net worth individuals. EPWA fosters an inclusive environment that offers opportunities for our associates to learn, grow and enhance their skills to take on new challenges to progress in their professional careers.
Job Summary:
The Managing Director, AI & Data Platforms is the senior leader accountable for EP Wealth’s enterprise AI enablement and Data platform strategy and execution - building the foundations that power analytics, automation, and agentic workflows across the firm. This leader will own the “platform layer” for data and AI (e.g., Snowflake-based enterprise data platform, data governance and quality, AI tool enablement, and agentic integration patterns), ensuring EP can safely and measurably scale AI-driven productivity and client/advisor impact.
We are seeking a hands-on, cloud-native player/coach who can set a multi-year vision while also rolling up their sleeves to deliver near-term outcomes. This role partners closely with Product, Salesforce/CRM, Engineering, Cyber/InfoSec, Legal/Compliance, and business leaders to enable an “agentic enterprise” -including EP’s strategic partnership focused on building a digital workforce and a growing ecosystem of approved AI tools.
Key Responsibilities:
Strategy, Governance, and Value Leadership
- Define and execute a multi-year AI & Data platforms strategy and roadmap aligned with EP’s business priorities, operating model, and regulatory obligations.
- Establish and run adaptable, effective AI and data governance: standards, patterns, intake/prioritization, exception management, and measurable value tracking (time saved, cycle time reduction, adoption, quality, and risk metrics).
- Translate platform investments into business outcomes (efficiency, growth enablement, risk reduction) and recommend prioritized initiatives and funding.
- Partner with Product and Business to translate platform investments into business outcomes (efficiency, growth enablement, risk reduction) and recommend prioritized initiatives and funding.
Enterprise Data Platform Leadership (Snowflake + Data Products)
- Own the enterprise data platform operating model: architecture, engineering standards, scalability, reliability, and cost management (FinOps for data).
- Lead the evolution from “pipelines and reporting” to data products (well-defined datasets with owners, SLAs, documentation, quality controls, and reusability).
- Partner with domain teams (e.g., Wealth Management Services, Finance, Operations, Marketing) to identify and prioritize high-value data products and decisioning use cases.
Salesforce + Agentic Data Enablement (Data Cloud / Data360 / Agentforce)
- Serve as the primary technology counterpart to Salesforce for agentic + data integration, ensuring Salesforce’s data environment is reliably grounded in governed enterprise data.
- Define patterns for data synchronization, master data strategy, lineage, and real-time/near-real-time availability for agentic workflows and operational automation.
- Ensure data accessibility for agents is intentional: least privilege, purpose-based access, auditable retrieval, and clear boundaries.
AI Platform Enablement (Enterprise LLMs, Vendor AI Tools)
- Build EP’s “AI platform” capabilities that make AI usable at scale:
- role-based prompt libraries and reusable workflows
- custom GPT/assistant patterns (guardrails, tool access, retrieval boundaries)
- evaluation and monitoring patterns (quality, hallucination risk, escalation thresholds)
- Own the strategy and integration approach for third-party AI tools used by advisors and employees, ensuring these tools align with EP’s data handling, compliance, and audit requirements.
- Drive an “enablement + adoption” model: training, playbooks, success measures, and feedback loops.
AI Risk, Privacy, and Compliance-by-Design (Partner with Cyber/Legal/Compliance)
- Partner with Cyber/InfoSec and Legal/Compliance to implement data protection and AI guardrails:
- data classification and handling rules
- DLP-aligned usage patterns
- retention/auditability for AI outputs where required
- vendor risk reviews and controls for third-party AI tools
- Ensure agents and AI workflows are explainable and auditable: what data was accessed, why, what output was produced, and what human approvals occurred.
MLOps / “AgentOps” (Operationalizing AI)
- Establish repeatable operational practices for AI solutions:
- model/prompt versioning and change control
- testing/evaluation gates before production
- monitoring, incident response, and rollback playbooks
- human-in-the-loop escalation for higher-risk workflows
- Create a measurable operating cadence (weekly metrics, quality review, and continuous improvement)
Team Leadership and Program Operations
- Lead and mentor a high-performing AI & data organization (data engineering, analytics enablement, platform engineering, and AI enablement roles).
- Establish platform KPIs (data quality SLAs, pipeline reliability, time-to-insight, adoption, AI resolution rates, cost/unit economics).
- Manage budget and vendor relationships to ensure efficient, effective platform coverage.
