Note: The job is a remote job and is open to candidates in USA. WEX is seeking a Sr. Staff AI Platform Engineer to serve as a Systems Architect for enterprise AI and agentic workflows. The role will define long-term platform strategy, build context-layer infrastructure, evaluate emerging AI architectures, standardize interoperability protocols, and lead architecture and engineering excellence across the organization.
Responsibilities
- AI Platform Strategy & Context Retrieval: Define and own the 3–5 year technical roadmap for our high-scale, AI-ready Data Lakehouse. This platform must be explicitly optimized for AI Agent operations and efficient context retrieval, delivering low-latency, high-throughput data access essential for vector databases and LLM-driven applications
- Systems & Agentic R&D: Prototype and benchmark emerging trends in the AI ecosystem. You will evaluate next-generation architectural patterns such as Multi-Agent Orchestration, autonomous long-term memory management, and specialized Agent Evaluation frameworks to ensure the platform remains at the cutting edge
- Engineering Excellence: Set the gold standard for code quality, CI/CD, and system design across the organization. You will lead cross-functional architecture reviews and serve as the final escalation point for the most complex technical bottlenecks
- Agentic Ecosystem Enablement: Design the platform-level interfaces required for Agentic workflows, focusing on standardized "Host-to-Server" communication and tool-execution environments. This includes building robust "Human-in-the-Loop" (HITL) triggers and fail-safe mechanisms for autonomous actions
- Contextual Infrastructure: Build the "Context Fabric" that allows AI agents to securely discover, access, and interpret enterprise data. You will architect systems that move beyond basic search into Reasoning-based Retrieval, where the platform understands the intent behind an agent's query
- Protocol & Trend Standardization: Implement and advocate for emerging standards like the Model Context Protocol (MCP) to ensure interoperability. You will stay ahead of trends such as Small Language Models (SLMs) for edge-compute and Agentic RAG, ensuring the platform can pivot as the industry evolves
Skills
- Expert Software Engineering: 15+ years in software engineering
- You are an expert in Java or Scala (distributed systems focus) and Python
- Deep experience building extensible frameworks, high-throughput APIs, and libraries used by other developers
- Hands-on experience with the latest trends in agent development, such as Multi-Agent Orchestration (using frameworks like LangGraph or CrewAI) and the transition from static RAG to Agentic RAG
- Knowledge of the Model Context Protocol (MCP) and other emerging standards that allow AI agents to interact with diverse data sources and tools in a plug-and-play manner
- Experience building "AI-native" CI/CD features, such as automated LLM-based evaluations (evaluating agent reasoning paths in the build pipeline) and Automated Root-Cause Analysis for system failures
- Understanding of how to build automated workflows that pause agent actions for human approval, ensuring safety and governance for autonomous systems
- Expert-level experience with GitOps workflows (e.g., ArgoCD or Flux) to ensure that all platform configurations—including AI prompt templates and model parameters—are versioned, audited, and automatically reconciled
- Mastery of Terraform
- Proficiency in designing complex pipelines (e.g., GitHub Actions, GitLab CI) that integrate automated testing, security scanning, and deployment gates for high-availability systems
- Experience with OpenTelemetry (OTel) to build deep visibility into distributed systems
- Deep proficiency in navigating and configuring the AWS and Azure Management Consoles
- You have a comprehensive understanding of how to architect, secure, and optimize core services (IAM, EC2/VMs, S3/Blob, and specialized AI/ML service suites) natively within both ecosystems
- Proven ability to build platform layers that bridge AWS and Azure, allowing for seamless deployment and management across a multi-cloud environment
- Experience using cloud-native tools (AWS CloudWatch, Azure Monitor, Cost Explorer) to manage platform health, security posture, and spend at an enterprise scale
- A proven track record of "leading by influence"—driving adoption of new technologies across multiple autonomous teams
- Ability to communicate complex architectural trade-offs (e.g., "Latency vs. Consistency") to both C-suite executives and engineers
- Bachelor's or Master's degree in Computer Science (Distributed Systems focus) preferred
Benefits
- Health, dental and vision insurances
- Retirement savings plan
- Paid time off
- Health savings account
- Flexible spending accounts
- Life insurance
- Disability insurance
- Tuition reimbursement
Company Overview
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