Senior Engineering Manager – AI CoE

About Redwood

Redwood Software helps the world's largest enterprises automate mission-critical business processes. Our platforms support complex enterprise workloads across ERP systems, business applications, cloud infrastructure, and on-premises environments.

Redwood is building reusable AI infrastructure, agentic capabilities, developer tools, and platform services that enable enterprise-ready AI features across its product portfolio.

Role Mission

As a Senior Engineering Manager, you will lead the engineering team responsible for delivering Redwood's shared AI platform and agentic capabilities.

You will translate the AI strategy into an executable engineering roadmap, build a high-performing team, and ensure the delivery of secure, scalable, observable, and commercially valuable AI systems. The role requires sufficient technical depth to guide architecture and challenge engineering decisions while remaining accountable for people's leadership, execution, team health, and delivery outcomes.

Key Responsibilities

• 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.

Scope and Expected Impact

• 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.

Must Have Qualifications

• 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 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.

Strong Plus

• 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.

What Success Looks Like

• Builds a healthy, appropriately staffed engineering team with clear responsibilities and strong technical ownership.
• Delivers AI platform and agentic capabilities predictably while maintaining security, reliability, quality, and cost discipline.
• Creates measurable adoption of shared AI capabilities across Redwood's product engineering teams.
• Establishes clear engineering standards for AI evaluation, observability, governance, and production readiness.
• Improves team effectiveness, delivery predictability, and operational performance.
• Develops capable engineers and reduces organizational dependency on individual contributors.
• Provides senior stakeholders with a clear, evidence-based view of progress, risks, dependencies, and investment requirements.