SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Aledade is seeking a Staff Software Engineer to lead the development of their AI Platform. This is a high-impact individual contributor role focused on building scalable, production-grade infrastructure for AI and machine learning systems.
In this role, you will:
- Identify and develop scalable, performant solutions for AI platform infrastructure
- Work cross-functionally to shape product strategy and technical execution
- Establish and maintain code architecture standards and quality practices
- Mentor and coach other engineers on the team
- Set and uphold engineering processes that support high-quality, reliable systems
- Design and operate production platform infrastructure serving multiple engineering teams
- Lead technical decision-making on both short-term and long-term business value
You will be responsible for foundational work on AI infrastructure, including LLM model gateways, foundation model APIs, and managed AI platform integrations. Your expertise will be critical in designing observability, operational practices, security, and cost management for shared infrastructure handling sensitive or regulated data.
REQUIREMENTS:
- BS/BTech or higher in Computer Science, Engineering, or related field
- 8+ years of production-level experience building highly scalable systems
- 4+ years as a trusted technical decision-maker in a team setting, balancing short-term and long-term business value
- 4+ years working with SQL or other database querying languages on large multi-table datasets
- Proven experience architecting, developing, and deploying large-scale distributed systems at scale
- Experience with cloud technologies (AWS, Azure, GCP)
- Experience building CI/CD pipelines
- Strong familiarity with server-side web technologies (Java, Python, Scala, C#, C++, Go)
PREFERRED QUALIFICATIONS:
- Experience building or operating production platform infrastructure, distributed systems, developer platforms, or high-availability shared services
- Experience with LLM infrastructure, model gateways, foundation model APIs, or managed AI platforms (AWS Bedrock, Azure OpenAI, Google Vertex AI, or similar)
- Strong cloud infrastructure experience, preferably AWS, with Kubernetes, containers, and infrastructure-as-code
- Experience designing observability and operational practices (metrics, distributed tracing, logging, alerting, SLOs, dashboards, failure handling)
- Experience designing APIs and self-service platform capabilities for engineering teams
- Experience with identity, access control, secrets management, security, and governance for shared infrastructure
- Experience with cost management, usage attribution, quotas, budgets, or capacity management for shared platforms
- Experience operating systems handling sensitive or regulated data
- Ability to evaluate architectural tradeoffs across reliability, performance, security, cost, scalability, and developer experience
