Sr Staff AI Platform Engineer

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.

Key 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.

Specialized AI & Agentic Responsibilities

• 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.

Qualifications & Experience

• Software Engineering Foundation

  • Expert Software Engineering: 15+ years in software engineering. You are an expert in Java or Scala (distributed systems focus) and Python.
  • Systems Architecture: Deep experience building extensible frameworks, high-throughput APIs, and libraries used by other developers. You prioritize building \"software-defined infrastructure\" over manual configuration.

• Agentic Development & Emerging Trends (Specialized Plus)

  • Agentic Design Patterns: 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.
  • Protocol Interoperability: 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.
  • AI-Ops Integration: 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.