Platform Engineer II

Upbound Group•Plano, TX

About The Position

The Platform Engineer II is a lead individual contributor responsible for developing, maintaining, and evolving the company’s Internal Developer Platform, cloud infrastructure, identity services, event-driven architecture, and AI-powered engineering capabilities. The role leads the design and implementation of scalable platform solutions across cloud environments, establishes engineering standards and best practices, and advises engineering teams. It partners with Development, Security, Architecture, and DevEx teams to improve developer productivity, platform reliability, and software delivery velocity. A significant focus is building and operating AI-powered platform capabilities, including LLM-based automation, agentic workflows, retrieval systems, and developer experience enhancements integrated throughout the software development lifecycle. The engineer also drives adoption of modern identity platforms and event-driven architectures for customer-facing and internal engineering systems.

Requirements

Bachelor’s degree in Computer Science, Engineering, or equivalent practical experience.
At least 6 years of experience in Platform Engineering, DevOps, Site Reliability Engineering, or Software Engineering.
At least 2 years of hands-on experience building, deploying, or operating AI/LLM-powered solutions.
Strong software engineering background designing and building production applications, APIs, services, or developer tooling.
Proficiency in Python, Go, or equivalent programming languages used for platform and automation development.
Deep expertise with AWS and/or GCP cloud platforms.
Expert knowledge of Kubernetes, container orchestration, and cloud-native infrastructure patterns.
Expert-level experience with Terraform and infrastructure automation.
Extensive experience implementing advanced CI/CD pipelines and GitOps workflows.
Hands-on experience with Keycloak, Entra ID, Auth0, or comparable identity platforms.
Strong understanding of OAuth2, OpenID Connect (OIDC), SAML, JWTs, RBAC, and enterprise identity architectures.
Production experience operating Kafka or enterprise event-streaming platforms.
Strong understanding of distributed systems, event-driven architecture, consumer scalability, partitioning, replication, resiliency patterns, and schema governance.
Experience designing and deploying RAG-based solutions.
Understanding of embeddings, vector databases, semantic search, prompt engineering, and context retrieval techniques.
Experience building AI agents, tool-calling frameworks, or agentic workflows.
Hands-on experience integrating LLMs with engineering workflows, source control platforms, ticketing systems, or operational tooling.
Experience implementing MCP server integrations or equivalent AI tool integration frameworks.
Experience evaluating, monitoring, and improving AI system performance in production environments.
Understanding of AI governance, guardrails, validation strategies, and human-in-the-loop workflows.
Applicants must be authorized to work for any employer in the United States; visa sponsorship or sponsorship takeover is not available.

Nice To Haves

Experience productionizing agentic systems with permissions, auditability, rollback strategies, failure handling, and human approval workflows.
Experience integrating AI systems across multiple enterprise engineering platforms, such as GitHub, Jira, ServiceNow, Confluence, and Backstage, in a governed production environment.
Familiarity with LangGraph, CrewAI, Semantic Kernel, OpenAI Agent Framework, or similar orchestration frameworks.
Experience building automated AI evaluation pipelines, benchmark datasets, regression tests, and continuous quality feedback loops.
Experience building Backstage-based or equivalent Developer Portals with catalog ownership, software templates, scorecards, and adoption metrics.
Experience using engineering productivity data to identify platform friction, prioritize improvements, and measure developer experience outcomes.
Experience supporting regulated environments and security compliance frameworks.

Responsibilities

Lead the design, implementation, and management of scalable, highly available, and secure platform services across AWS and GCP.
Drive Infrastructure as Code adoption through reusable Terraform modules, automation frameworks, and platform standards.
Architect and implement advanced CI/CD pipelines using GitOps principles, progressive delivery, and engineering automation.
Design and operate platform observability solutions for monitoring, logging, tracing, alerting, and SLO management.
Troubleshoot critical platform incidents and perform root-cause analysis.
Mentor engineers and establish engineering best practices across development and platform teams.
Design and evolve the Internal Developer Platform, including self-service capabilities, platform APIs, golden path templates, service scaffolding, and Developer Portal experiences.
Define and measure platform adoption using DORA metrics, developer productivity metrics, and platform usage analytics.
Build reusable platform services, SDKs, APIs, CLIs, and automation frameworks to improve engineering efficiency.
Design, implement, and operate enterprise identity services for workforce, customer, and machine-to-machine authentication.
Partner with security teams on authentication, authorization, federation, RBAC, and governance standards.
Lead adoption and operational excellence of identity platforms, including Keycloak and Entra ID.
Design and operate Kafka-based messaging and event-streaming platforms.
Establish event-driven architecture standards for schema management, resiliency patterns, and event governance.
Partner with application teams to build scalable asynchronous architectures using event-streaming technologies.
Design, build, and operate AI-powered platform services integrated into the software development lifecycle.
Develop RAG and agent-based workflows that interact with enterprise systems such as GitHub, Jira, Confluence, CI/CD systems, and observability platforms.
Implement MCP-based integrations and AI tooling for safe and efficient engineering workflow automation.
Build AI-powered developer experiences, including engineering copilots, automation assistants, deployment assistants, and developer productivity tooling.
Design guardrails, evaluation frameworks, approval workflows, and observability solutions for AI systems.
Instrument and monitor AI applications using metrics for latency, token usage, retrieval quality, hallucination rates, user feedback, and operational effectiveness.