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.

Key Responsibilities

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.

The base pay range represents the anticipated low and high end of the pay range for this position. Actual pay rates will vary and will be based on various factors, such as your qualifications, skills, competencies, and proficiency for the role. Base pay is one component of WEX's total compensation package. Most sales positions are eligible for commission under the terms of an applicable plan. Non-sales roles are typically eligible for a quarterly or annual bonus based on their role and applicable plan. WEX's comprehensive and market competitive benefits are designed to support your personal and professional well-being. Benefits include health, dental and vision insurances, retirement savings plan, paid time off, health savings account, flexible spending accounts, life insurance, disability insurance, tuition reimbursement, and more. For more information, check out the "About Us" section.

Pay Range: $140,200.00 - $185,800.00

Sr Software AI Engineer 3 - Context Engineering