Responsibilities

We are looking for an experienced Assistant Vice President to lead initiatives on our Workday platform and drive innovation across the enterprise. In this role, you will oversee Workday-related projects, partner with stakeholders across the organization, and enhance platform capabilities to meet evolving business needs. This position requires strong Workday expertise, leadership skills, and the ability to manage complex, cross-functional initiatives.

Key responsibilities include:

• Lead Workday projects from planning to execution, ensuring alignment with business objectives and delivery timelines.
• Oversee system upgrades, enhancements, and the deployment of new Workday modules.
• Manage configuration, testing, and rollout of updates across Workday HCM, Financials, Talent Management, Recruiting, and other modules.
• Partner with business units to understand needs and translate them into scalable Workday solutions.
• Drive integrations between Workday and enterprise systems to ensure seamless data flow and operational efficiency.
• Support day-to-day platform stability, user experience, and performance monitoring.
• Lead strategic initiatives around Workday adoption, reporting optimization, and process improvements.
• Provide program management support for enterprise-level initiatives involving Workday usage.

Qualifications

• Bachelor’s degree in Business Administration, Information Technology, Finance, or a related field.
• 5+ years managing enterprise applications, including 3+ years of hands-on Workday experience.
• In-depth knowledge of Workday functionality and modules.
• Strong technical expertise in Workday integrations (web services, APIs).
• Experience with Workday implementation, consulting, or administration.
• Ability to quickly learn complementary technologies (e.g., Azure tools, Data Visualization, Data Analytics).
• Excellent written and verbal communication skills with proven ability to partner across multiple stakeholders.
• Strong problem-solving and troubleshooting skills.
• Comfortable working in a fast-paced, dynamic environment with multiple priorities.

1. Infrastructure Operations and Support
   • Support the implementation, administration, monitoring, and troubleshooting of enterprise infrastructure services, including cloud platforms, servers, storage, network-connected systems, and identity services.
   • Assist in incident investigation, root-cause analysis, remediation planning, and execution of corrective actions to improve platform stability, resiliency, and service performance.
   • Perform routine operational activities such as patch validation, system health checks, access administration, configuration updates, and preventive maintenance in alignment with established standards.
   • Identify opportunities to automate repeatable operational tasks using scripting and platform-native tooling to improve efficiency and reduce manual effort.
2. Technical Documentation, Validation, and Communication
   • Create and maintain technical documentation, including system configurations, operational procedures, architecture support materials, support runbooks, and recovery instructions.
   • Contribute to test planning, validation activities, and documentation required for infrastructure changes, deployments, and controlled technology updates.
   • Prepare status updates, technical summaries, and project communications for IT leadership, project teams, and business stakeholders as appropriate.
   • Develop end-user and administrator guidance materials to support technology adoption, self-service enablement, and operational consistency.
3. Project Delivery and Cross-Functional Collaboration
   • Collaborate with infrastructure, security, application, and service management teams to support project delivery across multiple business functions, locations, and time zones.
   • Support technical initiatives involving Microsoft 365, Entra ID, Intune, endpoint management, cloud administration, server lifecycle activities, and network-related improvements.
   • Engage with vendors and managed service partners as needed to coordinate issue resolution, service delivery activities, and implementation support.
4. Continuous Learning and Compliance Awareness
   • Build and expand working knowledge of cloud infrastructure, enterprise systems administration, identity and access management, endpoint services, and operational support disciplines.
   • Develop familiarity with regulated environment requirements, including change control, validation, audit readiness, and GxP-aligned infrastructure practices where applicable.

• 2–4 years of professional experience in infrastructure engineering, cloud operations, systems administration, or a related technical discipline
• Hands-on experience supporting at least one major cloud platform, such as Microsoft Azure, AWS, or Google Cloud Platform
• Working knowledge of Windows Server and/or Linux administration, including user access, configuration management, and basic performance troubleshooting
• Familiarity with virtualization and compute platforms such as VMware, Hyper-V, or cloud-native virtual machine services
• Foundational understanding of networking concepts, including DNS, DHCP, TCP/IP, VPN connectivity, firewalls, and load balancing
• Experience using monitoring, service management, ticketing, and documentation platforms to support operational processes and issue resolution
• Strong analytical, troubleshooting, written communication, and cross-functional collaboration skills

• Experience supporting infrastructure or platform services within a regulated industry, such as pharmaceuticals, biotechnology, healthcare, or life sciences
• Exposure to scripting, automation, or infrastructure-as-code tools such as PowerShell, Python, Bash, Terraform, or similar technologies
• Familiarity with identity and endpoint management technologies, including Microsoft Entra ID, Intune, Active Directory, or related enterprise platforms
• Understanding of backup, disaster recovery, business continuity, and high-availability concepts for enterprise environments
• Knowledge of ITIL-aligned service management processes, including Incident, Problem, Change, and Request Management
• Relevant technical certifications in cloud, infrastructure, endpoint management, or virtualization are a plus

• Own and maintain the firm's AI risk framework, covering model risk, data privacy, adversarial threats, third-party AI, and regulatory compliance.
• Develop and enforce AI usage policies in collaboration with Legal and Compliance, including acceptable use, data classification requirements, and prompt handling standards.
• Maintain an inventory of AI tools deployed firm-wide — both sanctioned and shadow — and assess associated risk profiles.
• Provide regular AI risk reporting to the Head of Technology Risk and senior leadership, including emerging threat trends, vendor posture changes, and control gaps.
• Monitor the evolving regulatory environment for AI (EU AI Act, SEC guidance, DORA, NY DFS) and advise on compliance obligations and required controls.

• Lead security and risk assessments of vendors introducing AI capabilities into existing or new platforms, including evaluating model transparency, data handling practices, and auditability.
• Develop and maintain a structured AI vendor evaluation framework, incorporating criteria for model governance, output reliability, data residency, and incident response obligations.
• Partner with Procurement and Legal to ensure AI-specific provisions are reflected in vendor contracts, including data usage restrictions, model change notifications, and liability terms.
• Maintain a tiered risk register of third-party AI integrations, with ongoing monitoring for material changes to vendor AI functionality, architecture, or ownership.
• Engage directly with vendor security and product teams to assess AI-related controls and drive remediation of identified gaps.

• Conduct threat modeling for AI systems and integrations, including risks from prompt injection, model inversion, training data poisoning, and adversarial inputs.
• Evaluate AI-specific attack surfaces introduced by LLM integrations, agentic workflows, and MCP-connected data sources.
• Collaborate with infrastructure and application teams to embed AI security controls into deployment pipelines and system design reviews.
• Assess risks associated with AI-generated content, including deepfake vectors, synthetic phishing, and automated social engineering in the context of financial services.
• Contribute to the firm's broader security architecture by ensuring AI components are assessed within the existing control framework.

• Serve as the security and risk point of contact for the firm's internal AI deployments, including Claude Enterprise and any future platform integrations.
• Evaluate data retention, access control, and logging practices for AI platforms to ensure alignment with the firm's compliance and eDiscovery obligations.
• Provide risk assessments for proposed AI use cases across the firm, including a structured framework for approving, conditionally approving, or declining adoption.
• Support audit and compliance reviews related to AI, including evidence collection and engagement with regulators or external assessors as required.
• Develop and deliver AI security awareness content for technology staff and end users.

• Bachelor's degree in Computer Science, Information Security, Data Science, or a related field; advanced degree a plus.
• At least 7–10 years of experience in information security, technology risk, or a related field, with a minimum of 3 years focused on AI systems, machine learning security, or AI governance.
• Deep understanding of the AI and LLM landscape, including foundation model architecture, agentic systems, RAG pipelines, and the risk implications of each.
• Hands-on experience evaluating AI platforms and products, including the ability to assess vendor claims about model behavior, data handling, and security controls with appropriate skepticism.
• Familiarity with AI risk frameworks and emerging standards, including NIST AI RMF, MITRE ATLAS, OWASP LLM Top 10, and ISO/IEC 42001.
• Experience with vendor risk management in a regulated financial services environment, including contract negotiation support and third-party security assessments.
• Knowledge of relevant regulatory frameworks including DORA, SOX, SEC cybersecurity disclosure rules, and GDPR/CCPA as they apply to AI data flows.
• Strong technical skills sufficient to evaluate AI system architecture, API security, data pipeline design, and access control models without reliance solely on vendor documentation.
• Excellent communication skills, with the ability to translate highly technical AI risk concepts into clear, decision-ready language for senior leadership, Legal, and Compliance.
• Experience operating in a Microsoft-first environment, including familiarity with Entra ID, Azure, and M365 security tooling, is a strong plus.
• Ability to work independently, manage competing priorities, and operate effectively in a fast-paced, lean team environment
• Relevant certifications such as CISSP, CISM, CRISC, or emerging AI-focused credentials a plus

• Design, build, and operate the firm’s AI platform, enabling developers to build and deploy Python-based AI applications;
• Implement and manage Azure AI Foundry environments: model deployments, AI hubs, project workspaces, and access controls;
• Integrate and operationalize third-party AI APIs (Anthropic Claude API, Azure OpenAI) with secure access patterns, API gateway controls, rate limiting, and cost monitoring;
• Build internal developer tooling and SDK scaffolding to accelerate AI application development across the firm.

• Build and maintain data pipelines using Azure Data Factory and Azure Databricks to serve AI application data needs;
• Implement vector search and document retrieval infrastructure (Azure AI Search) to support RAG-based applications;
• Manage structured and unstructured data stores including Azure Data Lake, Azure SQL, and Cosmos DB.

• Provision and maintain secure, scalable infrastructure on Azure (primary) and AWS using Infrastructure-as-Code (Terraform or Bicep);
• Build and maintain CI/CD pipelines for AI application deployment via Azure DevOps or GitHub Actions;
• Manage containerized workloads using Docker and Kubernetes (AKS) for AI application hosting and API services;
• Drive Secure DevOps practices including secrets management, dependency scanning, and policy enforcement.

• Implement data access controls, encryption, and audit logging aligned with SEC and FINRA requirements;
• Design MNPI data segregation controls and enforce AI governance guardrails including content filtering and usage logging for all AI API integrations;
• Manage identity and access for AI platforms using Azure Entra ID, RBAC, and managed identities;
• Collaborate with Compliance and Legal to ensure AI usage policies are enforced at the infrastructure level.

• Produce architecture designs, runbooks, and technical documentation for all platform components;
• Support application developers building AI-powered applications with platform guidance and troubleshooting;
• Partner with solution architects, data engineers, and business stakeholders to translate requirements into infrastructure.

• Bachelor’s degree in computer science, Computer Engineering, or related field (Master’s degree is a plus);
• 8+ years in infrastructure engineering, cloud platform engineering, or data engineering;
• Demonstrated experience building shared platforms or developer services in an enterprise environment;
• Azure expertise: Azure AI Foundry, Azure Data Factory, Azure Databricks, AKS, Azure API Management, Azure Key Vault, Azure Entra ID;
• Strong Python skills: backend services, REST APIs (FastAPI or Flask), and automation scripting; PowerShell for infrastructure tasks;
• Infrastructure-as-Code: Terraform and/or Bicep; container orchestration with Docker and Kubernetes;
• Experience integrating LLM APIs (Anthropic Claude, Azure OpenAI) in production including token cost management and observability;
• RAG pipeline experience: vector search (Azure AI Search or pgvector), document processing, and retrieval patterns;
• Familiarity with LLM application frameworks such as LangChain or Semantic Kernel;
• Cloud security fundamentals: RBAC, managed identities, private endpoints, Key Vault, and network segmentation;
• DevSecOps practices: secrets management, SAST tooling, and secure CI/CD pipeline design;
• Familiarity with AWS (Bedrock, S3, IAM) and agentic application frameworks (AutoGen, LangGraph);
• Experience with Microsoft Fabric, Databricks Unity Catalog, or Azure Synapse;
• Azure certifications: AI-102, DP-203, or AZ-305;
• Prior experience in investment banking or regulated financial services environments.