Senior Engineering Manager – AI CoE

Overview

Lead the engineering team responsible for Redwood's shared AI platform and agentic capabilities, translating AI strategy into an executable engineering roadmap while building a high-performing team and ensuring delivery of secure, scalable, observable, and commercially valuable AI systems.

What you'll do

• Lead one or more engineering teams responsible for AI platform services, agentic capabilities, developer tools, and product integrations.
• Translate business and product priorities into a clear engineering roadmap, delivery plan, and team-level objectives.
• Recruit, onboard, manage, and retain engineers with the skills required to build production-grade AI and agentic systems.
• Establish clear ownership, performance expectations, decision-making responsibilities, and development plans across the team.
• Drive predictable execution through effective planning, dependency management, risk identification, and operational readiness.
• Partner with senior individual contributors to guide architecture and technical direction without becoming a bottleneck for engineering decisions.
• Ensure consistent engineering practices across AI evaluation, observability, model governance, security, reliability, performance, and cost management.
• Collaborate with Product, Architecture, Security, Data, Legal, and product engineering teams to deliver enterprise-ready AI capabilities.
• Balance shared platform investments with customer-facing requirements and immediate product priorities.
• Monitor delivery, quality, platform adoption, incidents, operational performance, and engineering effectiveness.
• Establish an engineering culture focused on ownership, accountability, continuous improvement, collaboration, and customer outcomes.
• Conduct regular one-on-one meetings, performance discussions, and career development planning.
• Communicate engineering progress, risks, dependencies, and investment requirements to senior stakeholders.
• Maintain sufficient technical involvement to understand architectural risks, challenge decisions, and support critical engineering outcomes.
• Own engineering outcomes for a critical AI platform domain or multiple related engineering teams.
• Convert broad strategic priorities into measurable plans, milestones, and delivery commitments.
• Make organizational, staffing, prioritization, and delivery decisions with medium- to long-term impact.
• Create alignment across senior engineers, Product, Architecture, Security, and business stakeholders.
• Improve delivery predictability, platform adoption, engineering quality, and operational reliability.
• Scale the organization through clear ownership, repeatable engineering practices, and strong technical leadership.
• Ensure the team can deliver effectively without depending excessively on any single individual.

What you'll need

• 15+ years of experience in software engineering, with at least 4+ years in an engineering people-management or team-leadership role.
• Experience leading one or more engineering teams responsible for complex software products or platform services.
• Demonstrated experience hiring, managing performance, coaching engineers, and supporting career development.
• Track record of delivering enterprise SaaS, cloud, distributed systems, platform, ML, or AI products into production.
• Strong working knowledge of generative AI, agentic architectures, LLM-based applications, orchestration, and production deployment considerations.
• Understanding of agent workflows involving tool use, retrieval, enterprise data, APIs, memory, guardrails, and human oversight.
• Experience managing technical roadmaps, delivery risks, dependencies, incidents, quality, and operational performance.
• Strong understanding of software architecture, APIs, cloud-native systems, automated testing, CI/CD, security, and observability.
• Ability to guide technical decisions and architecture discussions while empowering senior engineers to own implementation.
• Strong communication skills with the ability to explain technical and business trade-offs to senior stakeholders.
• Experience working effectively with distributed and cross-functional teams.
• Ability to operate with high autonomy and manage ambiguity in a product or high-scale technology environment.

Nice to have

• Experience establishing or scaling an AI platform, agentic engineering team, Center of Excellence, or developer platform function.
• Hands-on exposure to LangGraph, AutoGen, Semantic Kernel, or custom agent orchestration frameworks.
• Experience with AI evaluation platforms, model gateways, vector databases, prompt lifecycle management, or AI observability tools.
• Understanding of responsible AI, model governance, privacy, compliance, and enterprise security requirements.
• Experience in enterprise middleware, workflow orchestration, ERP, automation, or integration platforms.
• Experience balancing greenfield platform development with the modernization of existing enterprise products.
• Experience working in regulated, security-sensitive, or high-availability environments.