Sr Software AI Engineer 3 - Context Engineering

WEX•California, CA

23h•$140,200 - $185,800

About The Position

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.

Requirements

6+ years of software engineering experience with a focus on distributed systems.
Proficiency in Java or Scala and Python.
Experience building extensible APIs and libraries used by other developers.
Hands-on experience with agent development frameworks such as LangGraph or CrewAI and the transition from static RAG to Agentic RAG.
Knowledge of the Model Context Protocol (MCP) and how it allows AI agents to interact with diverse data sources.
Experience building "AI-native" features, including automated LLM-based evaluations within the CI/CD pipeline.
Understanding of Human-in-the-Loop (HITL) triggers to ensure safety in autonomous systems.
Experience with GitOps workflows (e.g., ArgoCD or Flux) to manage versioned platform configurations and AI prompt templates.
Proficiency in Terraform to build reusable modules that enforce organizational standards across cloud accounts.
Ability to design CI/CD pipelines (e.g., GitHub Actions) that integrate automated testing and security scanning.
Hands-on experience navigating and configuring AWS and Azure Management Consoles, including core services like IAM, EC2/VMs, and S3/Blob storage.
Experience using OpenTelemetry (OTel) to track system performance and AI-specific success metrics.
A proven track record of collaborating across autonomous teams to drive the adoption of new technologies.
Ability to clearly communicate technical trade-offs to both fellow engineers and stakeholders.

Nice To Haves

A preference for building automated, software-defined infrastructure over manual configuration.
Bachelor’s or Master’s degree in Computer Science (Distributed Systems focus) preferred, or equivalent deep industry experience.

Responsibilities

Contribute to the development of a high-scale, AI-ready Data Lakehouse optimized for AI Agent operations and low-latency context retrieval.
Hands-on prototyping of emerging architectural patterns, such as Multi-Agent Orchestration and autonomous long-term memory management.
Maintain high standards for code quality and CI/CD, participating in cross-functional architecture reviews and troubleshooting complex system bottlenecks.
Build platform-level interfaces for agentic workflows, focusing on "Host-to-Server" communication and tool-execution environments.
Develop systems that move beyond basic search into Reasoning-based Retrieval, helping the platform understand the intent behind an agent's query.
Implement emerging standards like the Model Context Protocol (MCP) and Agentic RAG to ensure interoperability between the platform and various LLM providers.