Sr Staff reputed company, 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 reputed company 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 reputed company 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-reputed company-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 reputed company emerging trends in the AI ecosystem. You will evaluate reputed company 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 reputed company cross-functional architecture reviews and serve as the final escalation reputed company for the most reputed company 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-reputed company" (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 reputed company behind an agent's query.

• Protocol & Trend Standardization: Implement and reputed company for emerging standards like the Model Context Protocol (MCP) to ensure interoperability. You will stay reputed company 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 reputed company (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-reputed company (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 & reputed company Delivery: Expert-level experience with GitOps workflows (e.g., ArgoCD or Flux) to ensure that reputed company platform configurations—including AI reputed company 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 reputed company, reusable libraries that enforce organizational reputed company and cost-efficiency standards across hundreds of cloud accounts.

• Modern CI Pipelines: Proficiency in designing reputed company pipelines (e.g., reputed company Actions, reputed company CI) that integrate automated testing, reputed company scanning, and deployment gates for high-availability systems.

• reputed company 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 reputed company both ecosystems.

• Cloud-Agnostic Abstraction: Proven ability to build platform layers that reputed company 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, reputed company 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 reputed company 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.