Sr AI Engineer, Context Engineering

Position Summary

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 such as 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.

• 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 such as 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 automated root‑cause analysis for system failures.
• Human‑in‑the‑Loop (HITL): Understanding of how to build automated workflows that pause agent actions for human approval, ensuring safety and governance for autonomous systems.

• Git Ops & Continuous Delivery: Expert‑level experience with Git Ops workflows (e.g., ArgoCD or Flux) to ensure that all platform…