Software Development Manager, ICON

The Intelligent Cloud Hosting (ICON) team is looking for a Software Development Manager who is passionate to work on the development of innovative software products which will help boost engineers productivity in Amazon, for improving services' posture towards security, availability, efficiency, compliance and maintainability. We aim to eliminate vast quantities of undifferentiated work for Amazon development teams - our opportunity for impact to tens of thousands of developers is enormous.

As the Software Development Manager for ICON's team in Dublin, you are a hands-on leader who guides engineers building the AI native factory that produces automation to operate on Amazon's production infrastructure. Your team owns the test pipeline that validates every workflow before it reaches production, builds workflows that run at Amazon scale, and contributes core capabilities to the factory. ICON operates critical services that power the Amazon shopping experience. Keeping those services healthy has required manual runbook authoring, interrupt-driven toil, and undocumented expertise held by individual engineers. The factory replaces that work with validated automation.

Engineers describe operational intent in natural language, and the factory produces a validated, production-ready workflow tailored to that service's unique context. An engineer describes a routine change, such as moving a service to a new instance type during the next maintenance window, and the factory generates, validates, and executes the workflow. Engineers state the outcome they need, and the factory supplies the operational knowledge, safety checks, and execution steps.

We are an AI-native team. We use AI across every aspect of our work, from workflow generation and validation to execution monitoring and continuous learning. The factory uses AI agents grounded in Amazon's operational history to generate, validate, and improve every workflow it delivers.

On this team, AI-native leadership is a daily practice. You use LLMs and agents in your own work, from drafting designs and analyzing operational data to reviewing code and preparing business reviews. You set the standard for how your team builds with AI, measure the productivity gains, and share what works across the organization. You understand how agentic systems behave in production: where they succeed, how they fail, and which guardrails keep them safe.

Key job responsibilities

- Own the technical vision, roadmap, and architecture for your team's scope, and deliver it end-to-end as the factory takes on new classes of operational work.

- Own the test pipeline that every generated workflow passes before production, and use automated evaluation and execution data to measure correctness and catch regressions before release.

- Partner with service teams to find high-toil operational tasks and turn them into validated, reusable workflows that run at Amazon scale.

- Contribute core capabilities to the factory, and align with partner teams on shared architecture and roadmap.

- Expand the share of operational work that agents own end-to-end, moving each workflow in stages from human-approved execution to supervised autonomy.

- Make the higher-stakes tradeoffs that come with AI in production: autonomy versus approval, speed versus safety, and model capability versus cost.

- Set a safety-first bar for production: separate fast experimentation from production-grade automation, establish operating best practices for traditional and AI-powered workloads, and own risk mitigation and escalations.

- Lead your team's adoption of AI development tools, measure the effect on delivery speed and quality, and share proven practices with partner teams.

- Hire, retain, and grow a multidisciplinary team of engineers and scientists who ship AI-native products at Amazon scale; set clear goals, give regular feedback, and manage performance.

- Earn adoption across service teams by making the factory effortless to use and trustworthy in production, and prove its value through measurable results in hours saved and execution success.

A day in the life

As the Dublin SDM, you balance engineering execution, technical judgment, and people leadership. A typical week might look like this:

• Reviewing a design for a new test pipeline stage that applies lessons from a recent operational event, so every generated workflow is checked against that failure mode.
• Meeting with a partner team about a repetitive operational task, and leaving with a plan to turn it into the next automated workflow.
• Aligning with partner teams on a shared platform capability your engineers are contributing.
• Pairing with a scientist on your team on how to score workflow quality automatically, so the platform improves with every run.
• Coaching an engineer through their first design review, helping them reason about blast radius and rollback for automation that runs in production. Your team uses AI in its own engineering work, from design and prototyping to testing and deployment. About the team Our team builds and operates the AI-native factory that produces automation for Amazon's production infrastructure. The Dublin team owns the factory's test pipeline and workflow delivery, and contributes core factory capabilities. We are an AI-native team that uses AI across every aspect of our work, from workflow design and code generation to validation and operational monitoring. ICON hosts Amazon's customer-facing websites and backend services, centralizing management and automation so developers across Amazon can focus on customer innovation. If you want to build at the intersection of AI, distributed systems, and operational automation, and build the safeguards that let engineers rely on AI-generated automation in production, we would like to hear from you.

Basic qualifications

- Knowledge of engineering practices and patterns for the full software/hardware/networks development life cycle, including coding standards, code reviews, source control management, build processes, testing, certification, and livesite operations

- Experience managing teams, or experience building complex software systems that have been successfully delivered to customers

- Experience designing, building, operating, and managing large-scale distributed systems or web services

- Track record of partnering with product, program, and technical leadership to define strategy and priorities

Preferred qualifications

- Experience in recruiting, hiring, mentoring/coaching and managing teams of Software Engineers to improve their skills, and make them more effective, product software engineers

- Experience in communicating with users, other technical teams, and senior leadership to collect requirements, describe software product features, technical designs, and product strategy

- Experience working with Data & AI related technologies, including, but not limited to, AI/ML, GenAI, Analytics, Database, and/or Storage

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