You will drive a unified product and transformation strategy that accelerates evolution toward a cloud-native, AI-first research ecosystem. Key initiatives include retiring legacy tooling, modernizing core runtimes, and creating scalable self-service workflows that shorten research-to-production from months to hours. This transformation enables AlphaGen's Investor Research Solutions & Services to focus on high-value quantitative partnership with the Portfolio Management Group (PMG), while Investor Research product (under your leadership) provides a robust foundation, reusability, and productized research capabilities to support and scale alpha generation efforts.

AlphaGen Investor Research Product is PMG's strategic research-technology engine. In this role, you will lead the organization responsible for delivering a cloud-native, AI-powered, self-service suite of products that accelerates the end-to-end lifecycle of data, signals, and models all while working closely with the Alphagen platform team in providing the operational backbone required for reliability, resiliency, security, and scale.

Key Responsibilities:

Strategic Leadership & Transformation Execution

• Strategy Development: Develop and execute a cohesive engineering strategy that aligns AlphaGen's product direction with major transformation initiatives (e.g., cloud migration, AI-first workflows, standardization, legacy system retirement).

• Clear Ownership: Establish clear product ownership, accountability, and governance across the entire AlphaGen investor research product estate

Partnership Building & Cross-Functional Collaboration

• Cross-Organization Partnerships: Forge and nurture strong partnerships across stakeholders, broader Aladdin product and platform engineering organizations to drive shared roadmaps, shared outcomes, and frictionless execution of initiatives.

• Governance & Standards: Strengthen collaboration with internal governing bodies and central teams to ensure consistent standards, regulatory compliance, and seamless integration with broader enterprise systems.

User Engagement & Adoption (Self-Service by Default)

• User-Centric Design: Drive user adoption strategies in partnership with product management and key stakeholders, creating a 'single front door' experience for researchers to access data, tools, documentation, and support covering spectrum of research personas.

• Frictionless Pipeline: Deliver a frictionless research-to-production pipeline by productizing building blocks to enable data onboarding, signal development, model validation, production release, thereby enabling researchers to go from idea to production with minimal hand-offs.

Product Engineering Leadership (What Researchers Use)

• Core Frameworks & SDKs: Own and evolve the core libraries, frameworks, and SDKs that enable consistent signal and model development (e.g., standardized APIs, templates, archetypes), ensuring researchers have a cohesive development experience.

• AI-Enabled Tooling: Lead development of AI-powered research tools and automation (spec-to-signal workflows, validation agents, automated quality checks) to shift scaling from people-driven processes to platform-driven capabilities.

• Model Lifecycle Governance: Own end-to-end model lifecycle governance tooling, from experiment to validation to production, including configuration management, metadata tracking, reproducibility, and control frameworks.

Product Infrastructure Leadership (Systems the Platform Runs On)

• Foundational Systems Collaboration: collaborate with the Alphagen platform team to help optimize the foundational systems that power AlphaGen's products, including:

+ Compute & Runtime: scalable GPU/CPU execution environments, define efficient scaling policies, drive performance optimizations, and manage resource allocation models for diverse research workloads.

+ Observability & Operations: end-to-end telemetry and monitoring, enable proactive incident detection, and automate recovery mechanisms to ensure high platform uptime and resiliency.

+ DevOps & CI/CD: Champion infrastructure-as-code, robust deployment pipelines, and standardized build/test/release workflows to accelerate delivery and improve reliability of platform updates.

+ Reliability & Production Standards: apply resiliency patterns, and harden systems to enterprise production standards for security and stability.

+ Operational Automation: Expand automation for operational tasks (data quality checks, pipeline health monitoring, backfills, change management) to reduce manual intervention and error risk.

Legacy Modernization & Tech Stack Evolution

• Cloud Migration: key player in the end-to-end product modernization journey, including the migration from legacy systems to cloud-native architectures, and the timely retirement and consolidation of legacy tools.

• Next-Gen Platform Architecture: Drive an API-first, cloud-native platform design to improve scalability, performance, and interoperability across the ecosystem. Leverage AI/ML where it materially improves speed, quality, and control (e.g., intelligent automation, anomaly detection).

Market and Business Insight / 'North Star' Alignment

• Industry & Internal Insight: Stay attuned to market trends and internal demand signals, such as the rising need for adaptable alpha-generation platforms and AI-driven research capabilities.

• Define the North Star: Partner with senior leadership to define and communicate a clear business 'North Star' for AlphaGen Product Engineering , reduce operational barriers, enable rapid iteration, scale platform services, and ultimately capture more alpha opportunities for the firm.

Qualifications:

• Education: Bachelor's degree in Computer Science, Engineering, or a related field; or equivalent practical experience. An advanced degree is a plus.

• Strategic Engineering Leadership: Proven experience leading large-scale engineering organizations through product transformation initiatives (e.g., platform modernization, cloud migration, operating model changes). Demonstrated success in setting vision and executing across complex, multi-year technology programs.

• Technical Depth (Strong understanding of):

+ Alpha generation workflows or closely related quantitative research processes in finance/investments.

+ Cloud technologies and cloud-native architectural patterns (e.g., microservices, containerization, distributed computing e.g. Ray).

+ API-first platform design and developer experience best practices.

+ AI/ML applications for workflow automation, data validation, and operational excellence

+ Modern stack knowledge and skills , e.g. Python, Polars, Ray, MLFlow or equivalent technology

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