AI Infrastructure Implementation : Contribute to the development of a high-scale, AI-ready Data Lakehouse optimized for AI Agent operations and low-latency context retrieval.
Agentic R&D & Prototyping : Hands-on prototyping of emerging architectural patterns, such as Multi-Agent Orchestration and autonomous long-term memory management.
Engineering Excellence : Maintain high standards for code quality and CI/CD, participating in cross-functional architecture reviews and troubleshooting complex system bottlenecks.
Agentic Ecosystem Development : Build platform-level interfaces for agentic workflows, focusing on "Host-to-Server" communication and tool-execution environments.
Contextual Fabric Construction : Develop systems that move beyond basic search into Reasoning-based Retrieval , helping the platform understand the intent behind an agent's query.
Protocol Integration : Implement emerging standards like the Model Context Protocol (MCP) and Agentic RAG to ensure interoperability between the platform and various LLM providers.
Qualifications & Experience
Software Engineering & Systems
Experience : 6+ years of software engineering experience with a focus on distributed systems.
Core Languages : Proficiency in Java or Scala and Python .
Framework Development : Experience building extensible APIs and libraries used by other developers.
Software-Defined Infrastructure : A preference for building automated, software-defined infrastructure over manual configuration.
Agentic Development & AI Trends
Agentic Design : Hands-on experience with agent development frameworks such as LangGraph or CrewAI and the transition from static RAG to Agentic RAG .
Interoperability : Knowledge of the Model Context Protocol (MCP) and how it allows AI agents to interact with diverse data sources.
AI-Ops : Experience building "AI-native" features, including automated LLM-based evaluations within the CI/CD pipeline.
Safety & Governance : Understanding of Human-in-the-Loop (HITL) triggers to ensure safety in autonomous systems.
CI/CD & Cloud Operations
GitOps & Delivery : Experience with GitOps workflows (e.g., ArgoCD or Flux ) to manage versioned platform configurations and AI prompt templates.
Infrastructure-as-Code (IaC) : Proficiency in Terraform to build reusable modules that enforce organizational standards across cloud accounts.
Modern Pipelines : Ability to design CI/CD pipelines (e.g., GitHub Actions) that integrate automated testing and security scanning.
Cloud Mastery (AWS & Azure) : Hands-on experience navigating and configuring AWS and Azure Management Consoles , including core services like IAM, EC2/VMs, and S3/Blob storage.
Observability : Experience using OpenTelemetry (OTel) to track system performance and AI-specific success metrics.
Leadership & Education
Collaboration : A proven track record of collaborating across autonomous teams to drive the adoption of new technologies.
Communication : Ability to clearly communicate technical trade-offs to both fellow engineers and stakeholders.
Education : Bachelor's or Master's degree in Computer Science (Distributed Systems focus) preferred, or equivalent deep industry experience.
Pay Range: $140,200.00 - $185,800.00
Benefits
Health insuranceDental insuranceVision insuranceFlexible schedulePerformance bonus
Additional Information
As an AI Platform Engineer (SDE 3) , you will be a key builder of the high-performance software foundation that powers our enterprise AI. While your expertise lies in distributed systems and cloud-native architecture, you will apply these skills specifically to the "Context Layer" -the specialized infrastructure required to fuel next-generation Agentic AI workflows . You will work at the intersection of systems programming and modern AI infrastructure to solve practical problems in real-time data orchestration and multi-cloud compute optimization. This is a "platform-as-a-product" role where you build the tools and SDKs that enable other engineers to build autonomous agents with ease.
Wex · Remote