AI Hub Program Manager with Wealth management

Coforge; Oaks (PA); Hybrid; USD 180,000 - 240,000; Full time

Job summary

Coforge is seeking an AI Hub Program Manager to lead a cross-functional, multi-workstream initiative in wealth management. You will govern platform decisions, coordinate internal teams and cloud partners, and drive a scalable AI Hub roadmap from discovery to production.

You will balance infrastructure, governance, and rapid use-case delivery, while engaging executives and stakeholders to ensure measurable benefits.

Qualifications

• Experience leading large-scale platform or data/AI programs end-to-end.
• Proven track record managing multi-vendor, multi-team delivery ecosystems.
• Proficiency in agile at scale — SAFe, LeSS, or hybrid models.
• Strong program governance — RAID logs, milestone tracking, exec reporting.
• Ability to manage release and change in Microsoft-native environments.
• Experience navigating procurement, compliance gates, and security reviews in financial services.

Responsibilities

• Lead the AI Hub program across multiple workstreams with discipline and pace.
• Translate business goals into a living AI Hub roadmap and product vision.
• Define epics, features, and user stories for platform-level products.
• Balance foundational platform work with quick-win use cases for adoption.
• Co-design developer and business-user experiences (APIs, SDKs, no-code interfaces).
• Ensure governance and cross-team coordination with stakeholders.

Skills

AWS SageMaker

AWS Bedrock

GCP Vertex AI

Model registries

Vector databases

RAG pipelines

Orchestration (LangChain)

Semantic Kernel

Agent frameworks

API gateways

Observability tooling

Azure OpenAI GPT-4o

Mistral

LLaMA

Multi-cloud portability

Interoperability

Architectural literacy

Azure DevOps

GitHub Actions

Tools

Azure DevOps

GitHub Actions

Job description

Role: AI Hub Program Manager with Wealth management

Mode of Hire: Full Time

Skills Required:

Lead the technology

• Working knowledge of AWS AI/ML services (SageMaker, Bedrock) and GCP 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
• 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