Sr 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.

• 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.

• 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 Design Patterns:
  Hands‑on experience with the latest trends in agent development, such as Multi‑Agent Orchestration (using frameworks like Lang Graph 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…