AI Engineering Manager

Thrivent is seeking an experienced AI Engineering Manager to lead a high-performing team building scalable, secure, and compliant production AI systems. You will combine deep technical leadership with strategic oversight to drive agentic AI, LLM/RAG, and classical ML initiatives from ideation through production and operations across AWS and Azure. Success looks like resilient platforms, measurable business outcomes, and a healthy engineering culture. You’ll do this by: Leading and mentoring a team to deliver solutions spanning model development, agentic system design, and AI integration—ensuring observability, security, performance, and cost efficiency. Providing expertise in AI governance & Responsible AI aligned to financial-services expectations (e.g., NIST AI RMF, model risk management, GLBA privacy). Establishing operating policies, engineering standards, and SDLC/ML lifecycle practices that scale across teams. Translating company-wide objectives into budgets, roadmaps, OKRs, and measurable outcomes. Partnering with senior business and technology stakeholders to shape strategy, unblock delivery, and ensure compliance. Leading briefings and technical reviews that support IT objectives and executive decision-making.Duties & Responsibilities Designing Solutions

• Guide end-to-end architecture for AI systems (data → features → models → serving → monitoring), spanning AWS & Azure; review ADRs and ensure threat modeling, data lineage, and cost/perf trade-offs are explicit.
• Land decisions on RAG patterns, agent orchestration, vector indexing, prompt management, evaluation, and human-in-the-loop controls for regulated use cases.
• Sponsor reference architectures (Bedrock vs. SageMaker vs. EKS; Azure OpenAI vs. Azure ML vs. K8) and promote reuse via templates and golden paths. Developing Software
• Maintain hands-on credibility: read code, give architectural/code reviews, and insist on testing, type safety, CI/CD, IaC, and SRE hygiene (SLOs/error budgets).
• Uphold secure coding and data protection (PII handling, tokenization, KMS/Key Vault, private networking). Learning & Applying New Techniques
• Create space for tech spikes, brown-bags, and internal demos; track advances in LLM inference (vLLM/TGI/ONNX/TensorRT), guardrails, evals, and privacy-preserving ML.
• Anticipate trends (context-window management, retrieval quality, tool-use/agents, model distillation/quantization) and adapt roadmaps. Collaborating (Team, Cross-Team, Org)
• Strengthen agile practices (scrum/kanban), partner with Product on outcomes & metrics, and remove systemic blockers.
• Broker cross-product solutions (common feature store, prompt/eval service, model registry, shared vector search).
• Align the team to company priorities and risk posture; ensure auditability and runbooks. Setting Product/Platform Technology Strategy
• Co-create vision, strategy, and roadmaps for AI platform & products; influence backlog with business value cases and risk/cost transparency.
• Empower the team to choose fit-for-purpose tools within guardrails; rationalize where to buy vs. build. Defining Engineering Standards/Patterns
• Codify ML/LLM golden paths (templates for RAG, batch/online inference, streaming features).
• Champion observability-by-default (tracing, prompt logs, retrieval quality metrics, safety incident workflow).
• Contribute to enterprise frameworks (data contracts, model governance, rollout patterns). DevOps / MLOps / LLMOps
• Own the ML/LLM lifecycle: data quality → experiment tracking → model registry → gated promotions → canary/shadow → monitoring & drift response.
• Serve as escalation for production incidents; drive post-mortems, defect SLAs, and continuous improvement.
• Continuously evaluate latency, reliability, accuracy, safety, and cost. Selecting & Managing Technology Vendors
• Define selection criteria; lead RFPs/POCs; manage vendor performance, SOWs,
• Be an advocate to the team to define criteria’s for selecting the right platform/technology · Guides the team to build consensus on an approach and driving a build vs buy decision with the team. In a buy decision, works with the team to decide on criteria and vendor selection. · Execute or manage the overall technology solutions, platforms for the product groups and associated vendor relationships. Coaching Engineers o Holding regular 1:1s with team members and team meeting, o Provides constructive feedback, guidance and coaching to help engineers grow their skills and experience. o Provides career planning advice to engineers and creates development plans to help them achieve their career goals which leverages their skills and capabilities and provides them with learning opportunities. Recruiting/building talent o Leads the process of selecting and engaging the right consulting partners o Planning in advance for future people needs for the product groups, collaborate with the other teams on the recruitment process o Recruits, develops, and sustains a high-performing team whil

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