Sr Full Stack AI Engineer

About the role

We're a diversified financial services leader with more than $1.5 trillion in assets under management, administration and advisement as of year-end 2024. Our team of 22,000 people across 19 countries, serves more than 3.5 million individual, small business and institutional clients. We are a longstanding leader in financial planning and advice, a global asset manager and an insurer. Our unwavering focus on our clients and strong financial foundation connects each of our unique businesses - Ameriprise Financial, Columbia Threadneedle Investments and RiverSource Insurance and Annuities. Here, we foster meaningful careers, invest in the future, and make a difference for clients, institutions and communities around the world.

Responsibilities

• Design agentic workflows and multi agent orchestration patterns using approved AgentCore frameworks and runtimes.
• Build AI capabilities that leverage:
• Tool calling
• Retrieval augmented generation (RAG)
• Structured data semantics
• Reusable prompt and skill

• Value Realization & Platform Adoption
• Demonstrate measurable impact on research productivity, decision velocity, and analytical depth.
• Support adoption via reference implementations, documentation, and enablement materials.
• Provide architectural guidance to teams building on AgentCore to prevent fragmentation and "shadow AI."

• Enterprise Platform Enablement
• Integrate AI automation into existing enterprise platforms (e.g., CI/CD pipelines, SDLC tooling, cloud platforms).
• Establish reusable AI components, frameworks, and guardrails for product and engineering teams.
• Enable adoption through reference architectures, implementation patterns, and developer enablement.

• Value Realization & Measurement
• Identify and quantify productivity, quality, and cycle-time improvements driven by AI.
• Define KPIs and success metrics tied to SDLC efficiency, developer experience, and risk reduction.
• Support executive visibility into AI-driven outcomes and maturity progress.

• AI (AgentCore) Platform Maturity
• Design and build new AI capabilities, agents, and reusable skills on Ameriprise's AgentCore platform, aligned with standard reference architectures, lifecycle management, and governance controls.
• Contribute to AgentCore core services including agent runtime integration, memory patterns, observability, versioning, and gated deployment models.
• Ensure all AI capabilities comply with Ameriprise security, auditability, cost attribution, and responsible AI standards.

• Anthropic & Claude Enablement
• Lead enterprise adoption of Anthropic Cloud Code, establishing:
• Cloud Code implementation patterns
• Prompt, tool, and skill composition standards
• Agent safety and policy enforcement mechanisms
• Claude Interpreter and higher-order reasoning services to enable:
• Data exploration and analysis
• Multi-step reasoning workflows
• Secure tool invocation within agent boundaries

• Investment & Research Use Case Delivery
• Partner directly with Portfolio Managers and Research Analysts to identify, design, and implement value-add AI use cases, such as:
• Research summarization and synthesis
• Scenario, factor, and exposure analysis
• Structured and unstructured data interpretation
• Investment insight acceleration
• Convert exploratory research into repeatable, production-ready AgentCore solutions.

Requirements

• 8+ years of experience in software engineering, automation, or platform engineering roles.
• Proven hands-on experience implementing LLM based or agentic AI solutions in enterprise environments.
• Demonstrated expertise with Anthropic Cloud Code and advanced Claude services (including interpreter style workflows).
• Strong understanding of agent architectures, tool orchestration, and secure AI platform design.
• Experience working with investment, research, or Portfolio Management business users.
• Proficiency in Python and modern cloud native development patterns.
• Experience building AI platforms or shared AI services in financial services or regulated environments.
• Familiarity with portfolio management, investment research, or quantitative analytics workflows.
• Experience integrating AI solutions with enterprise data platforms and analytics stacks.
• Exposure to Responsible AI frameworks, model governance, and audit requirements.