AI Engineer
Senior AI Platform Engineer building high‑performance, cloud‑native foundations for next‑generation agentic AI workflows, focusing on distributed systems, real‑time data orchestration, and multi‑cloud compute optimization.
As an AI Platform Engineer (SDE 3), you will be a key builder of the high-performance software foundation that powers our enterprise AI. While your expertise lies in distributed systems and cloud-native architecture, you will apply these skills specifically to the "Context Layer"—the specialized infrastructure required to fuel next-generation Agentic AI workflows. You will work at the intersection of systems programming and modern AI infrastructure to solve practical problems in real-time data orchestration and multi-cloud compute optimization. This is a "platform-as-a-product" role where you build the tools and SDKs that enable other engineers to build autonomous agents with ease.
Key Responsibilities
AI Infrastructure Implementation: Contribute to the development of a high-scale, AI-ready Data Lakehouse optimized for AI Agent operations and low-latency context retrieval.
Agentic R&D & Prototyping: Hands-on prototyping of emerging architectural patterns, such as Multi-Agent Orchestration and autonomous long-term memory management.
Engineering Excellence: Maintain high standards for code quality and CI/CD, participating in cross-functional architecture reviews and troubleshooting complex system bottlenecks.
Agentic Ecosystem Development: Build platform-level interfaces for agentic workflows, focusing on "Host-to-Server" communication and tool-execution environments.
Contextual Fabric Construction: Develop systems that move beyond basic search into Reasoning-based Retrieval, helping the platform understand the intent behind an agent's query.
Protocol Integration: Implement emerging standards like the Model Context Protocol (MCP) and Agentic RAG to ensure interoperability between the platform and various LLM providers.
Qualifications & Experience
Software Engineering & Systems
Experience: 6+ years of software engineering experience with a focus on distributed systems.
Core Languages: Proficiency in Java or Scala and Python.
Framework Development: Experience building extensible APIs and libraries used by other developers.
Software-Defined Infrastructure: A preference for building automated, software-defined infrastructure over manual configuration.
Agentic Development & AI Trends
Agentic Design: Hands-on experience with agent development frameworks such as LangGraph or CrewAI and the transition from static RAG to Agentic RAG.
Interoperability: Knowledge of the Model Context Protocol (MCP) and how it allows AI agents to interact with diverse data sources.
AI-Ops: Experience building "AI-native" features, including automated LLM-based evaluations within the CI/CD pipeline.
Safety & Governance: Understanding of Human-in-the-Loop (HITL) triggers to ensure safety in autonomous systems.
CI/CD & Cloud Operations
GitOps & Delivery: Experience with GitOp
Posted June 22, 2026