Managing Director, AI & Data Platforms
New
USFull-TimeDirector
Salary not disclosed
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Job Details
- Experience
- 10+ years
- Required Skills
- SalesforceSnowflakeMLOps
Requirements
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (advanced degree preferred)
- 10+ years of experience in data platforms, analytics, engineering, or AI-related leadership roles
- Proven experience building and scaling cloud-based data platforms (e.g., Snowflake or equivalent)
- Strong knowledge of data governance, security, architecture, and FinOps practices
- Hands-on experience enabling enterprise AI tools, including LLM-based systems, prompt engineering, and workflow integration
- Ability to translate technical architecture into business outcomes and influence executive stakeholders
- Experience in regulated industries such as financial services or wealth management is highly desirable
- Familiarity with Salesforce ecosystems (e.g., Data Cloud, Agentforce, Financial Services Cloud) is a strong plus
- Experience leading teams, driving platform modernization, and managing cross-functional delivery
- Strong communication, leadership, and stakeholder management skills
Responsibilities
- Define and execute a multi-year AI and data platform strategy aligned with business, operational, and regulatory priorities
- Establish governance frameworks for data and AI, including standards, intake processes, prioritization, and value tracking
- Own and evolve the enterprise data platform, including architecture, engineering standards, scalability, performance, and cost optimization
- Drive the transition from traditional data pipelines to reusable, well-governed data products
- Enable Salesforce and CRM-based agentic workflows through secure data integration and governed access
- Build enterprise AI capabilities including LLM integrations, prompt libraries, and evaluation frameworks
- Partner with Cybersecurity, Legal, and Compliance to ensure AI solutions are secure and auditable
- Lead MLOps/AgentOps practices including monitoring, version control, and testing
- Manage and develop a multidisciplinary AI and data organization
- Drive adoption of AI tools through enablement programs and training
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