Job Description

We're not here to add to the noise. We're here to cut through it- with AI that actually works for marketers.

From AI-powered content creation to world-class CMS and the industry's most trusted experimentation platform, Optimizely is the tool modern marketers actually want to use. AI-Ready. Set. Go.

10,000+ brands including H&M, PayPal, and Zoom already get it. So do Gartner, Forrester, and IDC, who consistently recognize us as leaders in MarTech.

But here's the thing about building great products: it takes great people. Our 1,600+ Optimizers across 12 global offices are curious, collaborative, and refreshingly human. We don't do corporate speak. We do real conversations, big ideas, and genuinely care for the work we make together.

If you want to be part of a team that's shaping the future of marketing technology — and actually enjoys doing it — you're in the right place.

Find us on Instagram: @optimizely

Introduction

Professional Services delivery relies on patterns that repeat across many engagements: the same integration issue, the same onboarding friction, the same manual step. ESAI identifies these patterns and addresses them with AI, building solutions, agents, and automation that improve how engagements are delivered and how quickly customers realize value. Current platforms include UMP, which migrates customers to Optimizely from any source at scale, and MCP, an AI toolbox that accelerates CMS and Commerce upgrades. The role operates in two modes. You lead a solution through architecture, build, and enablement until it reaches general availability and Professional Services delivery can run it independently. After general availability, Delivery leads and you provide background support. On ESAI, you design and build the AI systems that advance Professional Services delivery. This is a small, growing team, so responsibilities span architecture, engineering, documentation, and enablement. We hold a high bar regardless of background. The role requires identifying where AI improves delivery economics, and building the solution

Job Responsibilities

• Architect AI-driven solutions. Design agents, integrations, and automation that change how engagements are delivered. Solutions should be reusable across accounts, not one-off scripts.
• Lead delivery through GA. Own a solution end to end, architecture through build, until general availability and Professional Services delivery can run it independently. After GA, Delivery leads and you provide support.
• Document solutions and processes. Document each solution so others can run, extend, or troubleshoot it without you. Documentation is part of the deliverable, not an afterthought.
• Drive adoption and gather feedback. Support the internal and Professional Services communities who use what you build, and gather their feedback to confirm solutions meet real needs. Adoption determines impact.
• Collaborate with architects and Delivery. Support fellow architects, and stay informed on Delivery and Product activity so solutions reflect field reality.

Knowledge and Experience

We prioritize systems thinking and applied AI engineering skills.

• Systems perspective. You can reason about an entire architecture, data, integrations, content, commerce, identity, and the seams between them. You've worked across more than one category: CMS, commerce, ERP, CRM, data platforms, integration, and identity. You see Professional Services delivery as a system that can be redesigned with AI.
• Root-cause analysis. You identify the actual source of recurring delivery friction, not just its symptoms, and can demonstrate your reasoning.
• Applied AI engineering. You have built and shipped production AI systems: agents, LLM-based workflows, and API integrations. You can design an approach and implement it yourself.
• Problem definition under ambiguity. You can scope and design a solution from incomplete or conflicting requirements, make sound tradeoffs, and defend your approach under pressure.
• Regional awareness. You understand the differences between EMEA and North America delivery, and design solutions for consistent execution across both.

Skills and knowledge

• Generative AI and agentic engineering. Experience with LLM integration: prompt engineering, RAG pipelines, vector stores, and an orchestration framework such as LangChain or LlamaIndex.
• Cloud and MLOps. Working knowledge of a major cloud platform (AWS, Azure, or GCP), including containers, CI/CD, and model monitoring.
• Production-grade programming. Proficiency in Python or a comparable language, sufficient to build and ship production systems.
• Systems and integration design. Experience with distributed systems, API design, event-driven architecture, and third-party integrations.
• Responsible AI and security. Understanding of bias mitigation, model risk, privacy, compliance, and core security practices: authentication, authorization, encryption, and secrets management.
• Optimizely product knowledge. An advantage, not a requirement. It accelerates onboarding and collaboration with Delivery and Product.

We value demonstrated skills and outcomes over years of experience. Relevant backgrounds include solutions architecture, technical consulting, platform engineering, and product engineering.

What success looks like

• You deliver against the roadmap. UMP, MCP, and future initiatives progress from concept to general availability and adoption.
• You turn challenges into solutions. You convert ambiguous problems into working solutions without waiting for a complete specification.
• Your work improves how we deliver. Solutions you build measurably improve delivery: fewer manual steps, faster engagements, and faster time-to-value for customers.

Optimizely is committed to a diverse and inclusive workplace. Optimizely is an equal opportunity employer and does not discriminate on the basis of race, national origin, gender, gender identity, sexual orientation, protected veteran status, disability, age, or other legally protected status.

Total Target Compensation range: $129,000-$220,000

Optimizely considers several factors when extending an offer, including but not limited to the role and associated responsibilities, a candidate's work experience, education/training, and key skills.

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