Staff AI Platform Engineer
J
JobgetherAI Infrastructure
Based in United StatesFull-TimeStaff
SalaryCompetitive compensation package with base salary, performance incentives, and equity opportunities.
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Job Details
- Experience
- 7+ years
- Required Skills
- DockerPythonJavascriptKubernetesTypeScriptgRPCReactRESTful APIsTerraformDistributed Systems
Requirements
- 7+ years of experience in platform engineering, backend systems, AI/ML infrastructure, or developer tooling, with a strong focus on building shared platforms and scalable systems.
- Proven experience designing AI platform capabilities such as agent orchestration systems, evaluation frameworks, retrieval infrastructure, context engineering tools, or model-serving abstractions.
- Strong proficiency in Python and/or TypeScript, with expertise in distributed systems, asynchronous architectures, and resilient backend services.
- Extensive experience designing APIs, SDKs, service contracts, and extensible platform interfaces using technologies such as REST or gRPC.
- Deep understanding of modern AI frameworks and agentic systems, including the ability to abstract and operationalize AI capabilities at scale.
- Experience with production AI operational concerns, including model integration, latency optimization, reliability engineering, observability, and cost management.
- Strong background in Infrastructure as Code and cloud-native technologies, including Kubernetes, Docker, and Terraform.
- Familiarity with modern web technologies and developer ecosystems, including JavaScript, TypeScript, and React.
- Demonstrated ability to build reusable engineering standards, internal platforms, and "golden path" developer experiences.
- Excellent communication, leadership, and stakeholder management skills, with the ability to influence technical strategy across teams.
- Bachelor's degree in Computer Science, Engineering, or a related field; advanced degrees are considered an asset.
Responsibilities
- Architect and build the core AI platform, including reusable APIs, SDKs, shared services, and infrastructure components that standardize AI development practices.
- Develop modular agentic platform capabilities such as orchestration frameworks, workflow primitives, retrieval systems, tool interfaces, and context management solutions.
- Design and maintain evaluation infrastructure to support AI experimentation, testing, quality measurement, and CI/CD-integrated offline and online assessments.
- Establish platform standards, schemas, and interoperability frameworks that enable seamless integration between AI services, tools, and applications.
- Build highly scalable and reliable serving, routing, and control layers that optimize latency, availability, governance, and cost efficiency.
- Implement observability, tracing, auditing, and policy enforcement mechanisms to ensure AI systems remain measurable, secure, and production-ready.
- Drive platform adoption by creating developer-friendly tools, documentation, templates, and reference implementations.
- Collaborate with engineering and product leaders to identify recurring challenges and transform them into reusable organizational capabilities and best practices.
- Influence long-term AI platform strategy, architecture decisions, and engineering standards across the organization.
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