AI Hub Program Manager with Wealth Management Experience

Role: AI Hub Program Manager with Wealth management

Location: Oaks, PA

Mode of Hire: Full Time

Skills Required:

Lead the technology

• Deep familiarity with Microsoft Azure AI stack — Azure OpenAI Service, Azure Machine Learning, Azure AI Foundry, Cognitive Services, Azure API Management, Azure Data Factory, Microsoft Fabric
• Working knowledge of AWS AI/ML services (SageMaker, Bedrock) and Google Cloud Platform AI services (Vertex AI) — sufficient to govern future-state architecture decisions
• Understanding of AI Hub platform components — model registries, vector databases, RAG pipelines, orchestration layers (LangChain, Semantic Kernel), agent frameworks, API gateways, observability tooling
• Awareness of LLM landscape — Azure OpenAI (GPT-4o), open-source models (Mistral, LLaMA), model selection tradeoffs for regulated environments
• Ability to evaluate multi-cloud portability and interoperability considerations as the client expands beyond Azure
• Enough architectural literacy to challenge design decisions, identify risks, and mediate between solution architects and business stakeholders

Program & Delivery Management

Run a complex, multi-workstream program with discipline and pace

• Proven track record managing large-scale platform or data/AI programs in enterprise environments — end-to-end, from discovery to production
• Experience managing multi-vendor, multi-team delivery ecosystems — internal client teams, Coforge squads, cloud partners, ISVs
• Proficiency in agile at scale — SAFe, LeSS, or hybrid agile-waterfall models suited to regulated enterprise delivery
• Strong command of program governance — steering committees, RAID logs, dependency mapping, milestone tracking, executive reporting
• Ability to manage release and change management in Microsoft-native environments (Azure DevOps, GitHub Actions)
• Experience navigating procurement, compliance gates, and security review cycles inherent to enterprise financial services programs

Product Thinking & AI Hub Roadmap Ownership

Translate business ambition into a living, prioritised platform roadmap

• Ability to define and own the AI Hub product vision — capabilities, personas, use case taxonomy, and evolution roadmap
• Skills in requirements elicitation from CxO, technology, and LOB stakeholders — distilling competing priorities into a coherent backlog
• Experience writing and governing epics, features, and user stories for platform-level products consumed by multiple internal teams
• Ability to balance foundational platform build (infrastructure, governance, security) against quick-win use case delivery that drives early adoption and exec confidence
• Comfort co-designing developer and business-user experiences on the AI Hub — APIs, SDKs, no-code/low-code interfaces, and self-service portals

AI Hub as Platform-as-a-Service (PaaS)

Move AI Hub from a project to a product

• Experience establishing internal platform-as-a-service models — onboarding workflows, service catalogues, tiered access, usage policies
• Ability to design and implement AI Hub operating models — who own what, how LOBs onboard, how usage is metered and governed
• Familiarity with FinOps principles — cost attribution, chargeback/show back models for multi-LOB AI platform consumption
• Understanding of platform scalability and tenant isolation in Azure-native environments
• Ability to define and track PaaS adoption KPIs — active LOBs, API call volumes, use cases in production, time-to-onboard metrics

AI Use Case Delivery - Asset & Wealth Management Domain

Drive use case build on the Hub

• Lead use case discovery workshops with LOB heads to identify, qualify, and prioritise AI opportunities on the platform
• Manage the design and delivery of AI use cases including but not limited to:
• Portfolio intelligence and investment research automation
• Client suitability and personalised wealth advisory
• Regulatory reporting and compliance automation (MiFID II, ESG, FATCA)
• Fraud detection and AML/KYC automation
• Document intelligence for onboarding, contracts, and fund documentation
• Risk analytics and market surveillance
• Ensure use case outcomes are measurable — define success metrics, track benefits realisation, and feed learnings back into the platform roadmap

Asset & Wealth Management Domain Knowledge

• Understanding of asset management operations — front office (portfolio management, trading), middle office (risk, compliance), back office (settlements, reporting, fund administration)
• Familiarity with wealth management client lifecycle — onboarding, suitability assessment, portfolio construction, advisory, reporting
• Awareness of regulatory landscape for asset & wealth management - MiFID II, UCITS, FATCA, CRS, ESG/SFDR, FCA conduct rules
• Understanding of model risk management in investment contexts - SR 11-7 equivalent principles, model validation, explain ability requirements
• Ability to frame AI value in business terms that resonate with investment professionals and wealth advisors — not just technology teams

LOB Adoption, Evangelism & Change Leadership

• Develop and execute an AI Hub adoption strategy — phased LOB onboarding, champions networks, centre of excellence model
• Design and run AI awareness and enablement programmes — executive briefings, developer enablement, business-user workshops, lunch-and-learns
• Build a network of AI Hub ambassadors within each LOB to sustain momentum beyond the core program team
• Create adoption collateral — use case showcases, ROI stories, platform capability demonstrations — tailored for different audiences
• Run executive adoption reviews — presenting adoption dashboards, value metrics, and next-wave opportunity pipelines to CxO stakeholders
• Navigate organisational resistance — identifying sceptics early, addressing concerns around job displacement, data privacy, and model trust