Principal Engineer, AI & Advanced Analytics

Ameriprise Financial Services, LLC•Minneapolis, MN

About The Position

Drive enterprise transformation by designing and implementing advanced AI and analytics solutions that address complex business challenges. Lead the design, engineering, and deployment of predictive analytics, machine learning models, and intelligent automation frameworks to enable data-driven decision-making at scale. Partner with Enterprise Architecture to define strategic vision, establish technical standards, and shape long-term roadmaps for AI and analytics adoption. As a technical thought leader, collaborate with cross-functional teams to deliver robust, cloud-native architectures leveraging AI technologies, big data platforms, and modern engineering practices. Champion innovation and ensure alignment with enterprise objectives through rigorous governance and architectural excellence.

Requirements

7-10+ years of experience in engineering and integration technology and/or software development, progressing in complexity and responsibility.
Ability to communicate and articulate technical information across various organizational teams and levels.
Proven high reasoning aptitude and ability to quickly understand and respond in complex operating environments.
Ability to coach and mentor experienced engineering staff.
Ability to think strategically and solve complex business and technical problems.
Advanced knowledge in Cloud-native architectures (Azure/AWS/GCP).
Experience with foundational AI services such as LLMs, Agentic AI, AWS Bedrock and Agent Core, and MLOps frameworks for CI/CD, model versioning, and automated retraining.
Expertise in ML frameworks (TensorFlow, PyTorch) and distributed computing (Spark, Ray), Data platforms (Databricks, Snowflake, BigQuery) and Container orchestration (Kubernetes, Docker).

Nice To Haves

Experience with DevOps tools and automation of software build, test, and deployment processes
Experience designing and implementing software delivery pipelines
Experience with developing and deploying containers, and use of container runtimes and registries
Experience with cloud providers such as AWS, Azure, or GCP, and deploying and managing applications at (one of) these providers.

Responsibilities

Architect and implement end-to-end AI solutions, including data ingestion, feature engineering, model training, deployment, and monitoring.
Enable predictive analytics, machine learning, and intelligent automation frameworks to support enterprise-scale decision-making.
Design cloud-native architectures leveraging AWS, Azure, or GCP for AI workloads and analytics platforms.
Integrate BI and advanced analytics tools into enterprise data ecosystems.
Independently oversee assignments in the design, detailed configuration, integration, and upgrade of existing and future technologies in Advanced AI and Enterprise Data Analytics.
Work with architecture and application teams to develop strategy and roadmaps, plan, design, review, and approve robust, stable, scalable, and manageable designs.
Perform problem diagnosis, initiate problem resolution, and provide ongoing life-cycle support for new technology deployments and upgrades.
Manage effective relationships and work in partnership with leadership, architecture, application team members, vendors, infrastructure, monitoring, procurement, security, and all other cross commit teams to deliver solutions that take advantage of cloud-first strategy, while ensuring that service level commitments are met.
Create and approve solutions in advanced AI and analytics solutions that address complex business challenges.
Oversee new solutions through proof-of-concept processes.
Identify and contribute to use case creation.
Recommend, deploy, and document design strategies and solutions for complex engineering problems.
Independently identify key issues, patterns, and deviations during analysis.
Recommend robust solutions utilizing pragmatic judgment, creativity, and in-depth technical knowledge and evaluation techniques.
Collaborate with application and architecture team members to ensure project scoping activities are aligned with architectural objectives and long-term portfolio roadmap.
Utilize performance data and historical metrics to effectively plan for growth needs; upgrades, migrations, optimizations, and new implementations; identify and resolve efficiency issues; and improve overall functionality.
Manage hand-over design solutions and provide testing support and validation prior to release of new and upgraded technologies.
Provide thought leadership and influence the refinement of processes, policies, and best practices to ensure the optimal performance and availability of technologies.
Promote re-use and develop consistent technical build, implementation, and support processes.
Oversee the validation of, and adherence to, defined standards.
Ensure designs are secure and the closure of open problem tickets.
Ensure improvements align with existing principles and Systems Development Life Cycle methodologies, engineering principles, and portfolio long term roadmap.
Continuously develop specialized knowledge and technical subject matter expertise by remaining apprised of industry trends, the direction of emerging technologies, and their potential value to the business.
Effectively present the value proposition of business-appropriate emerging technologies to technology leadership.