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Machine learning software development engineer (ai ops integration)

Dublin
Job Search Place Limited
Development engineer
Posted: 4 June
Offer description

Machine Learning Software Development Engineer, AI Ops Integration
Job ID: Amazon EU SARL, Irish Branch
As part of AI Operations Integration, we have a vision to transform Amazon Operations & Supply Chain into an AI Native organization by delivering intuitive and differentiated AI solutions that solve enduring operational challenges. We blend vision with curiosity and Amazon's real world experience to build rapidly AI capabilities. We accelerate our customers' businesses: internal operations teams across Amazon's global footprint through delivery of predictive analytics, LLMs and autonomous AI agents to automate decision making across Amazon's global operations and supply chain.
As a Machine Learning Software Development Engineer, you'll design and deploy production ready systems that combine traditional ML with modern agentic architectures to solve impactful operational problems at scale.
This role combines the excitement of a startup environment with the scale of Amazon Operations. You'll research state of the art open source and internal tools and will tackle highly ambiguous problems. If you thrive on ownership and dealing with ambiguity, are passionate about AI and want to fundamentally influence how Amazon Operations leverages AI, this role offers an extraordinary opportunity to make your mark.
Key Job Responsibilities

Build and deploy ML/LLM powered features across the full stack - from data pipelines and model serving to user facing internal tools.
Implement AI agent components that automate complex operational workflows across multiple systems and decision points.
Develop internal front end applications (dashboards, tools, products) that make AI outputs accessible to non technical operations users at scale.
Build integrations across internal APIs, databases, and MCP servers to enable multi system orchestration.
Contribute to the ML lifecycle: data pipelines, experimentation, deployment, monitoring, and evaluation.
Implement guardrails, evaluation frameworks, and human in the loop patterns for production AI systems.
Collaborate with operations teams, program/product, and scientists to translate requirements into shipped software.

About the Team
We're a cross functional team of machine learning engineers, ML & AI scientists and Technical PM focused on automating operational decisions in Amazon's supply chain.
Our charter is to identify high impact automation opportunities, build AI agents that can handle them reliably, and deploy these systems into production where they process real decisions daily.
The team operates with a build measure learn cycle. We work closely with operations partners to understand their problems, prototype solutions quickly, measure impact rigorously, and iterate based on real world performance.
Basic Qualifications

Bachelor's degree or equivalent.
Experience in machine learning, data mining, information retrieval, statistics or natural language processing.
Experience (non internship) in professional software development.
Experience designing or architecting (design patterns, reliability and scaling) of new and existing systems.
Experience programming with at least one software programming language.

Preferred Qualifications

Experience with full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations.

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability or other legally protected status.
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please refer to for more information. If the country/region you're applying in isn't listed, please contact your Recruiting Partner.
Posted: May 19, 2026
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