CAI Software
Staff Platform Engineer
Rol remoto de Platform Engineering con fit claro de ubicación del candidato.
Publicado11 jul 2026
Países elegibles1 país aceptado
Señal de senioritySenior
Modelo de trabajoRemoto
Ubicaciones aceptadas para candidatos
Estados Unidos
Resumen del rol
Staff Platform Engineer
Requisitos y responsabilidades
Contenido del rol extraído en secciones para revisar más rápido.
What You’ll Do
- Responsible for the execution of architecture, development, implementation and quality practices in support of delivery of CAI Parsable’s cloud platform
- Implementation of cloud platform, operational and architectural patterns and practices to enable key features and drive new feature development
- Participate in the selection of technology enablers to deliver performant, reliable and cost-optimized solutions
- Design, build, and maintain cloud platform capabilities, aligned with DevOps and operational practices to rapidly and safely deploy new features to our platform
- Collaborate with Product, Professional Services, and other stakeholders to develop new cloud-platform features and products
- Create technical documentation for accessing, integrating, and using CAI Parsable platform capabilities
- Act as a senior technical team member in the delivery of CAI Parsable’s cloud platform; gain and consistently maintain deep knowledge of our cloud platform and implementation technologies
- Serve as a senior technical team member and subject matter expert within the platform engineering team and the larger engineering organization
- Improve and build observability, uptime, and readiness metrics to detect problems and maintain our SLAs with our customers
- Implement DevSecOps tooling, processes and procedures to promote rapid iteration and reliable delivery of new and updated features
- Deliver quality work in an agile delivery model within a team environment
- Share your work and engineering best practices across the organization
- Design, develop, and operate agentic workflow infrastructure, including orchestration pipelines, tool-calling frameworks, and multi-agent coordination systems that power CAI Parsable’s AI-driven platform capabilities
- Establish platform patterns and guardrails for integrating LLM-based agents into production services, including prompt management, context window optimization, and safe tool execution
- Build and maintain MCP (Model Context Protocol) servers and API integrations that expose CAI Parsable’s platform capabilities as tools and context sources for AI agents
- Evaluate and adopt emerging agentic tooling, frameworks (e.g., LangGraph, AutoGen, CrewAI), and AI model APIs to accelerate delivery and maintain competitive platform capabilities
What We’re Looking For
- 8+ years experience delivering customer-facing platforms at scale
- 5+ years experience architecting, building and releasing production-grade within a SaaS environment
- 5+ years experience developing backend services capabilities leveraging Go and/or Node (Typescript)
- Demonstrated, hands-on mastery of the AWS suite of tools including Lambda, API Gateway, AppSync, EventBridge, ECS, S3, DynamoDB, DocumentDB, and AuroraDB
- Deep experience building product capabilities in a microservices architecture
- Ability to assert and defend opinions on the merits of differing solutions
- Ability to clearly explain technical concepts to non-technical audiences
- Hands-on experience designing and deploying agentic AI workflows, including multi-step reasoning pipelines, tool-use patterns, and autonomous agent orchestration in production environments
- Proficiency with LLM APIs (e.g., Anthropic Claude, OpenAI) and agentic frameworks such as LangChain, LangGraph, AutoGen, or CrewAI; experience implementing MCP servers and tool-calling integrations
- Strong understanding of prompt engineering, RAG (Retrieval-Augmented Generation) architectures, vector databases, and context management strategies for LLM-powered applications
- Experience building observability and evaluation frameworks for agentic systems, including monitoring for hallucination, latency, cost, and agent decision quality in production
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