In this role you design, build, and maintain internal AI-powered agents and automation workflows across Optimizely's AI stack to support reporting, operations, and cross-functional teams. You collaborate with data engineering, reporting, and operations to ensure reliable data feeds and scalable solutions. You apply engineering practices and cost-aware design to balance performance and efficiency, and you experiment to optimize speed and scalability. You champion AI tool adoption and share practical thought leadership to drive business impact.
Responsibilities
Design, build, and maintain internal AI agents, applications, MCP servers, and automation workflows
Develop and test scripts, APIs, and integrations connecting tools
Build and iterate on agents that support reporting, operations, and cross-functional workflows
Design and refine systems to ensure reliable, consistent outputs
Apply engineering practices, sound data structure design, and coding standards to deliver reliable, maintainable solutions
Consider cost models (token usage, compute, API costs) when balancing performance and efficiency
Conduct experiments and test different approaches to optimize performance, speed, and scalability
Collaborate with other technical teams to ensure clean, reliable data feeds to agents and automations
Troubleshoot and resolve issues relating to automations and AI processes
Author technical documentation to communicate designs, behavior, and processes
Partner with reporting analysts, business stakeholders, and adjacent technical teams to gather requirements
Champion the use of AI tools across the business
Evaluate and recommend AI tools, models, or frameworks appropriate to project needs
Key requirements
3–5 years of software development experience with AI/ML tools, APIs, or agent frameworks
Proficiency in at least one modern programming language (Python, Node) and understanding of core data structures
Experience integrating APIs, data pipelines, or building automation workflows
Familiarity with building or configuring AI agents including MCP-based integrations
Practical experience with prompt design/prompt engineering for agents or AI features
Understanding of AI/LLM cost models and how they affect design
Working knowledge maintaining and building with complex datasets
Strong problem-solving, technical judgment, and collaborative working style
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