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Proficiency in investment analytics, data architecture, and programming tools such as Python, SQL, Dash, and Power BI. Bachelor's degree required; advanced degree or certification (e.g., MBA, CFA, CAIA) strongly preferred. Builds the Business: Shapes long-term strategy through insight, foresight, and execution at scale. Drives Commercial Impact: Translates ideas into innovations that enhance portfolio performance and investment advantage. Delivers Through People: Develops talent and fosters a collaborative, high-performance team culture. Models Corporate Citizenship: Collaborates transparently across the enterprise and leads with integrity and shared purpose. Ensure initiatives are operationalized across the investment platform and embedded into daily decision-making. Manage, mentor, and develop a high-performing team, fostering a culture of intellectual curiosity, continuous learning, and applied experimentation. 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Map LLM/Gen AI capabilities to business needs; craft secure, scalable solution designs and guide the team through reviews and delivery 40% Hands-on engineering ? Rapidly prototype and code Gen AI proofs-of-concept; contribute production-quality Python (and occasional React) services running on AWS 20% Stakeholder & product collaboration ? Partner with the Product Owner and Investments stakeholders to refine requirements, set technical direction, and evangelize Gen AI opportunities. Liaise with Compliance, Privacy, and central GenAI teams. Continuously scan the Gen AI landscape?model releases, orchestration frameworks, prompt-engineering trends?and assess fit for our roadmap Establish engineering standards for code quality, observability, cost-efficient LLM usage, and secure development Mentor MLOps engineers and full-stack developers; foster a dynamic, collaborative culture that earns trust across technical and business teams Design of end-to-end solutions as an architect or senior engineer; proven Tech Lead for small, agile teams and the ability to work through others Advanced Python skills; experience building services that integrate with LLM APIs (OpenAI preferred). Solid understanding of AWS primitives (Lambda/ECS/Fargate, S3, IAM, networking) and secure-by-design principles. Comfort with MLOps concepts (model lifecycle, evaluation, monitoring) and CI/CD automation. Ability to communicate complex AI concepts to non-technical stakeholders and influence decision-makers with a calm, measured style. 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Makes recommendations for how to improve systems and data and drives those recommendations forward. Identifies business process improvements that address technology gaps. Consult on complex technology enabled recommendations to address gaps within business processes. Proactively identifies potential impact on business strategy. Documents existing processes, organizational structures, architectures and external systems. Consults on designing and developing of programs and tools to support the master data that drives customer experience across the organization. Supports the Solutions Engineer in implementation. Support can include but is not limited to; cutover plans, post-implementation support, any issues/defects, transition planning, and knowledge sharing efforts. Bachelor or Master`s degree in technical or business discipline or equivalent experience, technical degree preferred. Generally 8+ years of professional experience 5+ years of Master Data Management experience is preferred. In-depth knowledge of IT concepts, strategies and methodologies Proven experience designing solutions using cloud computing platforms & services (AWS, Lambda, Kubernetes) Working knowledge of data solutions such as Postgres and Mongo Atlas. Proven experience designing & building APIs (REST, SOAP, API Gateway, SpringBoot, etc.) Experience in agile software delivery (Scrum, Kanban, XP, etc.) Highly proficient in software engineering tools such as GIT Hub and Jira. Versed in diverse technologies and technical architecture principles and concepts. Extensive knowledge in layered systems architectures solutions and designs and shared software concepts. Knowledge of Application Monitoring tools like DataDog Strong consultative skills, including the ability to understand and apply customer requirements, including drawing out unforeseen implications and making recommendations for design, the ability to define design reasoning, understanding potential impacts of design requirements. 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