Mgr, Engineering

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Additional Information

Thrivent is seeking an experienced AI Engineering Manager to help lead the team that builds Thrivent's enterprise AI/ML platform - the capabilities that product delivery teams across the organization use to develop, deploy, and operate AI solutions spanning classical machine learning, LLM/RAG applications, and agentic systems. The platform is built on Databricks for machine learning and data science and on AWS for production serving, with Amazon Bedrock and SageMaker powering LLM workloads. The team operates in a product operating model: the platform is the product, delivery teams are its customers, and the team partners with Product Management to drive a capability roadmap measured by adoption and business outcomes. This leader will inherit a working foundation - governed workspaces, feature stores, retrieval and RAG services, and standardized batch and real-time model serving - and help drive the platform's next phases: evaluation and observability for LLM and agentic systems, automated governance fit for a regulated financial services environment, agent development capabilities, and platform cost management. The role will lead one or more platform capability areas (for example, model lifecycle and serving; GenAI and retrieval services; evaluation and observability; or agent platform), in partnership with AI engineering leadership and peer managers. Success in this role is defined by building resilient, observable, and cost-efficient platform capabilities that delivery teams adopt and that deliver measurable business value, while fostering a strong, healthy engineering culture. This leader will manage experienced engineers and specialists with significant autonomy, help establish the practices that shape how AI solutions are developed and operated at Thrivent, and lead critical conversations with internal and external partners in support of Thrivent's broader technology and business goals. DUTIES & RESPONSIBILITIES: Platform & Solution Leadership Review AI/ML and platform solution designs - classical ML, LLM/RAG, and agentic systems - and provide guidance on scalability, security, cost, and compliance across Databricks and AWS Shape reference architectures and reusable, paved-road patterns (training pipelines, deployment templates, retrieval services, repository bootstrapping) that make the right way the easy way for delivery teams Maintain strong technical fluency in AI/ML systems; able to read code and guide best practices across model integration, APIs, and platform services Apply strong problem-solving and analytical skills to guide teams through complex AI/ML and platform challenges Product Operating Model & Customer Enablement Partner closely with Product Management to define strategies, operating plans, roadmaps, targets, and measures for the platform capabilities the team owns Treat the platform as a product: understand the needs of Thrivent's delivery teams, drive adoption through enablement, documentation, and support, and measure success by customer outcomes Help set the product and platform technology vision in partnership with AI engineering leadership; represent the business value of platform investments and influence prioritization in the roadmap Support the team's agile practices; engage in sprint demos and understand both the outcomes sought and the technology delivered Build strong working relationships with peers across teams; proactively identify cross-team challenges and empower teams to solve them collaboratively Engineering Standards & Operational Excellence Champion engineering excellence across AI/ML and platform development, including code review, testing, CI/CD, MLOps , observability, and reliability practices Build evaluation and observability into the platform as first-class capabilities for LLM and agentic systems, including automated evaluation, drift monitoring, and tracing Partner with model risk, security, and compliance functions to automate governance: risk-tier classification, policy-gated promotion, and audit-ready evidence Own the operational health of the team's services: monitoring and alerting, serving as an escalation point for production incidents, and prioritizing work to maintain and continuously improve product health Technology Strategy & Vendor Selection Stay ahead of trends in AI/ML, platform engineering, and cloud (AWS), assess their impact on business capabilities and strategy, and create space for teams to experiment with and adopt emerging techniques Guide build-versus-buy decisions and tool selection for the team's capability areas; define selection criteria with the team and manage associated technology vendor and consulting relationships People Leadership Hold regular 1:1s and team meetings; provide constructive feedback, guidance, and coaching to help engineers grow their skills and experience Provide career planning advice and create development plans that leverage engineers' skills and capabilities and provide learning op

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