Stockx
AI Automation Engineer
1mo ago
Worldwidemachine learningapisworkflow automationai systemsloggingmonitoring
Design and build AI-driven automation systems to improve operational efficiency and unlock new business capabilities.
Requirements
- 4+ years of software engineering experience. 2+ years working AI/ML systems, LLMs, or intelligent automation.
- Experience with LLM APIs (OpenAI, Anthropic, etc.)
- Experience in vector databases and RAG systems
- Experience workflow orchestration tools
- Strong programming skills
- Experience building API integrations and distributed systems.
- Experience deploying systems in AWS, GCP, or Azure environments.
- Strong problem solving and system thinking skills
Other
- We are seeking an AI Automation Engineer to design, build, and scale intelligent automation systems that improve operational efficiency, reduce manual effort, and unlock new capabilities across the business.
- This role sits at the intersection of engineering, applied AI,workflow automation, and systems integration. You will be responsible for identifying high-impact automation opportunities and delivering production grade AI-driven solutions that integrate seamlessly into our technology ecosystem.
- You will partner cross-functionally with Engineering, Operations, Product, Data, and Security, as well as business stakeholders to transform manual and repetitive processes into intelligent, scalable systems.
- Design & deploy AI powered automation solutions using LLMs, machine learning models, and decision engines.
- Build agent-based systems, prompt pipelines, and retrieval augmented generation workflows.
- Develop autonomous or semi-autonomous systems that can take actions across internal tools and APIs.
- Create scalable workflow automation using APIs, event driven architecture, and orchestration frameworks.
- Integrate AI systems with internal platforms.
- Develop reusable automation components and internal tooling.
- Write clean, maintainable, production ready code.
- Build observability, logging, guardrails, and monitoring into AI systems.
- Ensure reliability, security, and responsible AI implementation.
- Measure performance impact.
- Continuously refine prompts, workflows, and models.
- Identify new automation opportunities across the organization.
- Implement guardrails, access controls, and safety mechanisms.
- Ensure compliance with data privacy and security policies.
- Partner with security teams on safe AI deployment.