Sr AI Engineer, Context Engineering

Position Summary

Sr. Staff AI Platform Engineer
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Design and build the high‑performance software foundation that powers the enterprise, focusing on distributed systems, cloud‑native architecture, and platform engineering for the Context Layer that fuels next‑generation Agentic AI workflows
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Operate at the intersection of systems programming and modern AI infrastructure, tackling 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; build tools, SDKs, and engines that enable hundreds of engineers to create autonomous agents with ease.

• AI Platform Strategy & Context Retrieval:
  Define and own the 3–5 year technical roadmap for a high‑scale, AI‑ready data lakehouse optimized for AI Agent operations and efficient context retrieval, delivering low‑latency, high‑throughput data access for vector databases and LLM‑driven applications.
• Systems & Agentic R&D:
  Prototype and benchmark emerging trends in the AI ecosystem, evaluating architectural patterns like Multi‑Agent Orchestration, autonomous long‑term memory management, and Agent Evaluation frameworks to keep the platform at the cutting edge.
• Engineering Excellence:
  Set the gold standard for code quality, CI/CD, and system design across the organization; lead cross‑functional architecture reviews and serve as the final escalation point for complex technical bottlenecks.

• Agentic Ecosystem Enablement:
  Design platform‑level interfaces for Agentic workflows, focusing on standardized Host‑to‑Server communication, tool‑execution environments, Human‑in‑the‑Loop 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, moving beyond basic search into reasoning‑based retrieval where the platform understands query intent.
• Protocol & Trend Standardization:
  Implement and advocate for emerging standards such as the Model Context Protocol (MCP) to ensure interoperability, while staying ahead of trends like Small Language Models (SLMs) for edge‑compute and Agentic RAG.

• 15+ years in software engineering, expert in Java or Scala (distributed systems focus) and Python.
• Deep experience building extensible frameworks, high‑throughput APIs, and developer‑friendly libraries, prioritizing software‑defined infrastructure over manual configuration.

• Hands‑on experience with Multi‑Agent Orchestration (e.g., Lang Graph or CrewAI) and transition from static RAG to Agentic RAG.
• Knowledge of the Model Context Protocol (MCP) and other emerging standards for plug‑and‑play AI agent data sources and tools.
• Experience building AI‑native CI/CD features, such as automated LLM‑based evaluations and root‑cause analysis for system failures.
• Understanding of Human‑in‑the‑Loop workflows that pause agent actions for human approval, ensuring safety and governance.

• Expertise with Git Ops workflows (ArgoCD or Flux) to version, audit, and reconcile platform configurations.
• Mastery of Terraform for building modular, reusable IaC libraries that enforce security and cost‑efficiency across many cloud accounts.
• Proficiency designing complex pipelines (Git Hub Actions, Git Lab CI) that integrate automated testing, security scanning, and deployment gates.
• Experience with Open Telemetry to build deep visibility into distributed systems and track AI performance metrics.

• Deep proficiency in navigating and configuring AWS and Azure services, including IAM, EC2/VMs, S3/Blob, and AI/ML services.
• Ability to build platform layers that bridge AWS and Azure for seamless multi‑cloud deployment.
• Experience using cloud‑native monitoring and cost‑optimization tools (AWS Cloud Watch, Azure Monitor, Cost Explorer) at enterprise scale.

• Track record of influencing adoption of new technologies across multiple autonomous teams.
• Ability to communicate complex architectural trade‑offs to C‑suite executives and engineers.

Bachelor’s or Master’s degree in Computer Science (Distributed Systems focus) preferred, or equivalent deep industry experience.

Health, dental and vision insurances, retirement savings plan, paid time off, health savings account, flexible spending accounts, life insurance, disability insurance, tuition reimbursement, and more. Compensation includes a competitive base pay range and potential bonus incentives.

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