Staff Software Engineer, AI Platform

New

AledadeAI infrastructure

Remote, United StatesFull-TimeStaff

Salary not disclosed

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Job Details

Experience: 8+ years of production-level experience as an engineer building highly scalable systems; 4+ years of experience acting as a trusted technical decision-maker in a team setting; 4+ years of experience working with SQL or other database querying languages on large multi-table data sets.
Required Skills: AWSPythonSQLGCPJavaKubernetesAzureCI/CDDistributed Systems

Hold a BS/BTech or higher in Computer Science, Engineering, or a related field.
Have 8+ years of production-level engineering experience building highly scalable systems.
Have 4+ years of experience acting as a trusted technical decision-maker in a team setting.
Have 4+ years of experience working with SQL or other database querying languages on large multi-table data sets.
Have experience architecting, developing, and deploying large-scale distributed systems.
Have experience with cloud technologies such as AWS, Azure, or GCP.
Have experience building continuous integration and continuous development (CI/CD) pipelines.
Have strong familiarity with server-side web technologies such as Java, Python, Scala, C#, C++, or Go.
Experience with production platform infrastructure, distributed systems, developer platforms, or high-availability shared services is preferred.
Experience with LLM infrastructure, model gateways, foundation model APIs, or managed AI platforms is preferred.
Experience with cloud infrastructure, Kubernetes, containers, and infrastructure as code is preferred.
Experience designing observability and operational practices, APIs, self-service platform capabilities, and security or governance for shared infrastructure is preferred.

Build and operate shared infrastructure that enables applications and AI agents to access foundation models securely and reliably.
Develop platform capabilities for model access, including model gateways, cloud AI services, provider integrations, routing, and fallback.
Build usage controls, observability, reliability, and model governance capabilities.
Develop scalable and performant solutions and evolve reusable, self-service platform capabilities.
Work across disciplines with engineering, product, security, infrastructure, and AI application teams to shape product strategy and execution.
Develop foundations for code architecture and quality.
Mentor and coach engineers.
Set and uphold engineering processes that support high-quality engineering.

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