Sr Staff AI Platform Engineer

WEXWEXUS•Remote, OH

5d•$220,000 - $255,800

About The Position

As a Sr. Staff AI Platform Engineer, you are first and foremost a Systems Architect. Your mission is to design and build the high-performance software foundation that powers the enterprise. While your core expertise lies in distributed systems, cloud-native architecture, and platform engineering, you will apply these skills specifically to the "Context Layer"—the specialized infrastructure required to fuel next-generation Agentic AI workflows. You will operate at the intersection of Systems Programming and Modern AI Infrastructure, solving "hard-tech" problems like real-time data orchestration, automated metadata evolution, and multi-cloud compute optimization. This is a "platform-as-a-product" role; you build the tools, SDKs, and engines that enable hundreds of other engineers to build autonomous agents with ease.

Requirements

15+ years in software engineering.
Expert in Java or Scala (distributed systems focus) and Python.
Deep experience building extensible frameworks, high-throughput APIs, and libraries used by other developers.
Prioritize building "software-defined infrastructure" over manual configuration.
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. You don't just write scripts; you build modular, reusable libraries that enforce organizational security and cost-efficiency standards across hundreds of cloud accounts.
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. You focus on tracking both system performance and business-centric AI metrics (e.g., success rates of autonomous tasks).
Deep proficiency in navigating and configuring the AWS and Azure Management Consoles.
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, or equivalent deep industry experience.

Responsibilities

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.
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.
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.
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.
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.
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.