Senior Manager- AI Strategy & Solutions

About Our Company

Ameriprise India LLP has been providing client based financial solutions to help clients plan and achieve their financial objectives for 20 years. We are part of Ameriprise Financial Inc., a US financial planning company headquartered in Minneapolis with a global presence and diversified financial services leader with more than $1.5 trillion in assets under management, administration and advisement as of year-end 2024. The firm’s focus areas include Asset Management and Advice, Retirement Planning and Insurance Protection.

Be part of an inclusive, collaborative culture that rewards you for your contributions, and work with other talented individuals who share your passion for doing great work. You’ll also have plenty of opportunities to make your mark at the office and a difference in your community. So, if you’re talented, driven and want to work for a strong, ethical company that cares, take the next step and create a career at Ameriprise India LLP.

Job Description

Integral role in supporting AIM’s data analysis, system control, and database needs. Provide strategic ideas and thought leadership. Has/can apply solid financial industry data systems knowledge to support decision and strategy problems in key business areas. Demonstrates strong technical/problem solving skills. Manages multiple projects collaboratively. Works with HR in recruitment and performance appraisal process. Mentor the team members and layout their growth path.

Role Summary

Leads the execution and operationalization of enterprise AI initiatives, ensuring AI strategy is translated into scalable, business-impacting solutions. Partners across business and technology teams to drive delivery, adoption, and measurable outcomes, while maintaining strong alignment to enterprise priorities and governance. Acts as a senior program leader with end-to-end accountability for execution, value realization, and executive alignment across priority AI initiatives

Key Responsibilities

1) Enterprise AI Execution & Operationalization: Translate enterprise AI strategy into actionable roadmaps, delivery plans, and implementation milestones. Drive end-to-end execution of priority AI initiatives across multiple business areas. Ensure AI solutions move from concept to pilot to scaled deployment, and ultimately to sustained adoption. Own delivery outcomes across initiatives, including scope, timelines, and measurable business impact.

2) Program & Portfolio Delivery: Lead a portfolio of AI use cases and programs across the enterprise. Establish and manage delivery discipline (prioritization, dependencies, timelines, risk mitigation). Ensure outcomes are tied to clear business value (e.g., productivity, growth, cost, risk). Drive integrated program plans, track KPIs and milestones, and proactively manage risks, dependencies, and execution challenges, escalating as needed.

3) Cross-Enterprise Partnership Partner with Business leaders across functions, Technology, Data & Analytics team, and Risk, Legal, and Governance teams.Align stakeholders around what gets delivered and how, while shaping execution priorities and ensuring alignment to enterprise AI strategy. Engage senior leaders, including ELT stakeholders, to drive alignment, inform decision-making, and ensure transparency on progress, risks, and outcomes. Act as a clear point of accountability for progress and results.

4) Technical & Solution Leadership: Provide informed direction on AI/ML and GenAI solutions to ensure fit-for-purpose design.Bridge strategy and execution by helping teams define use cases, select appropriate approaches/models, and ensure scalability and integration. Stay current on emerging AI capabilities and translate into practical application.

5) Adoption, Change & Value Realization: Drive business adoption of AI solutions, not just delivery. Define metrics and success measures and track realization of expected outcomes. Partner with HR / change / comms teams to support workflow integration and behavior change. Ensure value realization is clearly defined, tracked, and communicated, with a focus on measurable outcomes.

6) Governance & Responsible AI: Ensure adherence to enterprise AI governance, risk, and compliance frameworks. Operationalize responsible AI practices (e.g., transparency, fairness, controls). Support approval processes and risk assessments for AI deployments. Ensure alignment to the enterprise AI operating mo…