Description & Requirements

You will be the hands-on lead architect for our AI platform and the AWS infrastructure that powers it, reporting to the Director, Agentic Solutions. You will design, build, and operate the cloud foundation our production AI agents run on, going deep in AWS and Amazon Bedrock to make agents reliable, secure, and cost-effective at scale. You'll architect and ship the model gateways, agent runtime and orchestration, eval and observability frameworks, vector stores, and RAG services that the rest of XO builds on, and you'll still build agents end to end when the work calls for it. You will define requirements, rapidly prototype, iterate with stakeholders, and establish reusable architectures, standards, and patterns using the latest AI engineering methodologies, models, tools, and platforms. You're creative, innovative, self-motivated, and team-first, equally strong at problem-solving and collaborating across product, data, security, IT, and engineering teams. You will create scalable AI pipelines and workflows that let teams spend more time on high-value, creative, and strategic work. You will be a hybrid worker, collaborating with teams 3 days a week from the office; international travel to collaborate with global teams is an added bonus.

Responsibilities

• Own the platform foundation: design and run the cloud infrastructure our AI agents and solutions depend on, spanning account and network architecture, IAM, deployment patterns, observability, security, scaling, and cost.
• Go deep on the agent runtime: own model access and routing, agent orchestration, knowledge bases, and guardrails, and make the calls on when to use hosted models versus self-managed serving.
• Build and operate the platform services: stand up the compute, eventing, data stores, vector databases, and integration layer that connect agents to internal systems and EA Experiences workflows.
• Build production AI agents end to end: agent architectures, tool calling, MCP integrations, multi-agent orchestration, memory, evaluation, and guardrails, building alongside the team when the work calls for it.
• Standardize infrastructure as code and CI/CD: create reusable infrastructure-as-code modules and automated pipelines so the platform is repeatable and safe to change.
• Productionize and operate at scale: own the path from prototype to production, including integration with existing platforms and services, SLOs, reliability, cost controls, and incident response for AI workloads.
• Embed guardrails, safety, and FinOps: implement policies and evaluation frameworks for IP, privacy, and security, and define eval gates and cost and latency budgets that ship with every AI system.
• Partner across EA: work directly with teams to understand their processes and opportunities, then rapidly prototype, iterate, and productionize AI solutions that drive efficiency, expansion, and transformation.
• Evangelize and upskill teams: share best practices, host demos and workshops, and show teams how a solid platform and well-built agents expand what's possible in their work.

Your Qualifications

• 7+ years designing, building, and deploying production-grade software and the cloud infrastructure behind it, with strong software engineering, security, and infrastructure best practices.
• Deep, hands-on AWS experience is the core of this role, including Amazon Bedrock and related AI/ML services (model access, AgentCore, Knowledge Bases, Guardrails), IAM, networking (VPC), compute and serverless (Lambda, ECS/Fargate, containers), observability and telemetry (CloudWatch, tracing, structured logging), and secrets management.
• Strong infrastructure-as-code and CI/CD experience (CDK, Terraform, or CloudFormation), with a track record of building for reliability, scale, and cost efficiency.
• Strong Python development skills; working knowledge of at least one additional backend language (JavaScript/TypeScript and Node.js, Go, Java, or C#).
• Hands-on experience building production AI agents, not just prototypes, including prompt and context engineering; agentic architectures, tool calling, and Model Context Protocol (MCP); RAG, embeddings, and vector databases; and model evaluation, benchmarking, and guardrails.
• Experience building the integration layer behind AI products: APIs, auth, eventing, and data pipelines, and integrating AI into existing platforms and services.
• Enterprise architecture and microservices design; event-driven and async messaging patterns; API gateways and contracts.
• Extensive experience with spec-driven development and agentic AI coding tools such as Claude Code, Cursor, or Kiro.
• Experience navigating the legal, ethical, and security implications of AI, including data privacy, IP, and safety, and translating policy into engineering controls.
• Thrive working both collaboratively and independently, with excellent creative, critical thinking, and problem-solving skills, and a demonstrated ability to clearly articulate complex technical concepts.
• Experience working in a marketing organization or a gaming company is beneficial.

Pay Transparency - North America

COMPENSATION AND BENEFITS

The ranges listed below are what EA in good faith expects to pay applicants for this role in these locations at the time of this posting. If you reside in a different location, a recruiter will advise on the applicable range and benefits. Pay offered will be determined based on a number of relevant business and candidate factors (e.g. education, qualifications, certifications, experience, skills, geographic location, or business needs).

PAY RANGES

* British Columbia (depending on location e.g. Vancouver vs. Victoria) *$122,300 - $170,700 CAD

* Washington (depending on location e.g. Seattle vs. Spokane) *$133,100 - $178,400 USD

Pay is just one part of the overall compensation at EA.

In the US, we offer a package of benefits including paid time off (3 weeks per year to start), 80 hours per year of sick time, 16 paid company holidays per year, 10 weeks paid time off to bond with baby, medical/dental/vision insurance, life insurance, disability insurance, and 401(k) to regular full-time employees. Certain roles may also be eligible for bonus and equity.

For Canada, we offer a package of benefits including vacation (3 weeks per year to start), 10 days per year of sick time, paid top-up to EI/QPIP benefits up to 100% of base salary when you welcome a new child (12 weeks for maternity, and 4 weeks for parental/adoption leave), extended health/dental/vision coverage, life insurance, disability insurance, retirement plan to regular full-time employees. Certain roles may also be eligible for bonus and equity.

Post To

External careers site, Internal careers site

LinkedInID

1449