Manager, Engineering (AI & Agentic Platform)

QAD | Redzone is redefining manufacturing and supply chains through an intelligent, adaptive platform that connects people, processes, and technology into a single System of Action. Founded in 1979 and headquartered in Miami, Florida, the company serves manufacturers across more than 100 countries in industries including automotive, consumer products, food and beverage, high technology, industrial equipment, and life sciences.

The platform is built on three core pillars: Redzone (frontline empowerment), Adaptive ERP (the intelligent backbone of manufacturing operations), and ChampionAI (Agentic AI purpose-built for manufacturing). Together, these enable manufacturers to achieve measurable productivity, resilience, and growth — in as little as 90 days.

QAD | Redzone is seeking a technically strong Engineering Manager to lead our AI & Agentic Platform team. You will be responsible for building and scaling a production-grade AI platform for Manufacturing, combining strong people leadership with deep software engineering and AI expertise. This is a high-impact role at the intersection of modern cloud architecture, agentic AI systems, and industrial software.

LEADERSHIP PROFILE

The strongest candidates come from a Senior Software Engineer, Principal Engineer, or Tech Lead background who have grown into engineering management while retaining strong technical depth. You remain technically credible — you can engage deeply with architecture, debug hard problems, and set the bar — but you no longer need to be the strongest coder or AI specialist on the team.

KEY SKILLS

🏗 Software & Cloud Architecture

Microservices · Kubernetes · AWS · CI/CD · Observability

🤖 AI & Agentic Systems

LLMs · RAG · Orchestration · Tool Calling · Evaluation

🗂 Data Pipelines & Observability

Snowflake · Event-driven · Monitoring · Logging

🧑💼 Engineering Leadership

Coaching · Cross-functional · Technical Strategy · Delivery

Responsibilities

Define technical priorities for the engineering team and collaborate cross-functionally with Product and other engineering teams.; Coach, mentor, and develop software and AI engineers to support their professional growth.; Manage a small, focused team — including engineers more specialized than you in specific AI domains.; Challenge architecture and design decisions without becoming a bottleneck, uphold high standards for code quality, security, and reliability.; Balance innovation, delivery speed, and production-grade quality when building and scaling platform services.; Lead technical decisions spanning AI, backend, frontend, data, and cloud infrastructure.; Drive the design and deployment of robust software using modern cloud, container, and microservices architectures.; Oversee integration of Agentic AI elements — agent orchestration, RAG, tool calling, and workflows.; Implement standards for AI governance, evaluation, and agent observability.

Requirements

Total Experience of around 10+ years with 5+ years managing or technically leading a small engineering team.; Strong software engineering and architecture background (ideally from a Senior / Principal / Tech Lead trajectory).; Solid understanding of AI/LLM technologies — RAG, agent orchestration, tool calling, and workflows.; Hands-on exposure to building and deploying Agentic AI systems (not just conceptual knowledge).; Good command of data architectures and pipelines.; Experience with cloud architectures and modern cloud-native design.; Working knowledge of DevOps, CI/CD, containers, and observability.; Ability to challenge technical decisions across AI, backend, frontend, data, and infrastructure.; Proven record delivering production-grade software end-to-end.; Bachelor's degree in Engineering or a related technical field (required).; Master's degree, MBA, or equivalent graduate program (a plus).

Nice to have

Manufacturing, Industrial software, ERP, Supply Chain, WMS, or TMS experience.; AWS ecosystem familiarity — Bedrock, SageMaker, or equivalent.; Experience with Snowflake and/or Kubernetes.; Microservices and event-driven architecture patterns.; Background in AI governance, evaluation frameworks, and agent observability tooling.