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.
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.
CI/CD & Platform Ops Mastery (Core Focus)
GitOps & Continuous Delivery: Expert-level experience with GitOps workflows (e.g., ArgoCD or Flux) to ensure that all platform configurations—including AI prompt templates and model parameters—are versioned, audited, and automatically reconciled.
Infrastructure-as-Code (IaC) at Scale: Mastery of Terraform. You don't just write scripts; you build modular, reusable libraries that enforce organizational security and cost-efficiency standards across hundreds of cloud accounts.
Modern CI Pipelines: Proficiency in designing complex pipelines (e.g., GitHub Actions, GitLab CI) that integrate automated testing, security scanning, and deployment gates for high-availability systems.
Unified Observability: Experience with OpenTelemetry (OTel) to build deep visibility into distributed systems. You focus on tracking both system performance and business-centric AI metrics (e.g., success rates of autonomous tasks).
 Cloud Platform Expertise (AWS & Azure)
Cloud Console & Service Mastery: Deep proficiency in navigating and configuring the AWS and Azure Management Consoles. You have a comprehensive understanding of how to architect, secure, and optimize core services (IAM, EC2/VMs, S3/Blob, and specialized AI/ML service suites) natively within both ecosystems.
Cloud-Agnostic Abstraction: Proven ability to build platform layers that bridge AWS and Azure, allowing for seamless deployment and management across a multi-cloud environment.
Governance & Cost Optimization: Experience using cloud-native tools (AWS CloudWatch, Azure Monitor, Cost Explorer) to manage platform health, security posture, and spend at an enterprise scale.
Leadership & Education
Influence: A proven track record of "leading by influence"—driving adoption of new technologies across multiple autonomous teams.
Communication: Ability to communicate complex architectural trade-offs (e.g., "Latency vs. Consistency") to both C-suite executives and engineers.
Education: Bachelor’s or Master’s degree in Computer Science (Distributed Systems focus) preferred, or equivalent deep industry experience.