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Dell Technologies

Makes PCs, AI servers and data storage systems

Agentic Context Engineering Architect & AI Practitioner

AI Application EngineeringPosted on Nov 10
Full-timeHopkinton, MA, USA$232.8k - $320.1kIn-person

About the Job

Requirements

  • At least 12 years of experience in software engineering, platform engineering, distributed systems, or related technical leadership roles.
  • Strong hands-on experience with AI-assisted development, large language models, agentic systems, retrieval-augmented generation architectures, vector databases, knowledge systems, and enterprise AI platforms.
  • Deep understanding of context engineering concepts, including context construction, retrieval strategies, context composition, context lifecycle management, and prompt orchestration.
  • Experience building platforms, frameworks, or services that manage knowledge retrieval, agent memory, context assembly, or AI workflow orchestration at scale.
  • Strong understanding of AI system architecture, including embeddings, vector search, retrieval pipelines, memory architectures, agent orchestration, and tool integration patterns.
  • Ability to take ambiguous problems, define architectures and frameworks, and deliver practical solutions and platforms.
  • Experience working cross-functionally with engineering, architecture, product, security, and infrastructure stakeholders.
  • Strong written and verbal communication skills, with the ability to translate complex AI concepts into actionable technical guidance.

Responsibilities

  • Design, build, implement, and operate the enterprise AI Harness that enables secure, scalable, and governable AI-assisted software development across engineering organizations.
  • Develop and manage context engineering frameworks that optimize how agents acquire, interpret, retrieve, and use information throughout the software development lifecycle.
  • Architect and maintain context management services, including knowledge ingestion, context assembly, retrieval mechanisms, metadata management, and lifecycle governance for AI agent workflows.
  • Design and implement agent memory frameworks, including short-term, long-term, session-based, and persistent memory patterns that improve agent effectiveness, consistency, and task completion accuracy.
  • Build and maintain shared services, reusable frameworks, developer tooling, and platform integrations that accelerate AI adoption and ensure consistent agent behavior across engineering teams.
  • Partner with Product Security, Cybersecurity, Architecture, and Infrastructure teams to ensure context retrieval, memory systems, knowledge access, and agent interactions meet enterprise security and compliance requirements.
  • Drive context optimization strategies, including relevance scoring, retrieval quality measurement, context window efficiency, token utilization optimization, and knowledge effectiveness across AI development workflows.
  • Define standards and best practices for knowledge structures, metadata, content organization, context accessibility, and information quality to maximize agent performance and trustworthiness.
  • Drive engineering excellence across the AI harness ecosystem, including observability, evaluation frameworks, performance measurement, reliability, experimentation, and continuous improvement.

Desired Qualifications

  • Direct hands-on experience with agentic development platforms and tools such as GitHub Copilot, Claude Code, Cursor, Windsurf/Codeium, OpenAI, Anthropic, or equivalent technologies.
  • Experience designing or implementing context engineering frameworks, retrieval systems, knowledge graphs, semantic search, vector databases, or memory-centric agent architectures.
  • Familiarity with Model Context Protocol (MCP), agent toolchains, multi-agent systems, workflow orchestration frameworks, and enterprise AI governance practices.
  • Experience optimizing context utilization, retrieval quality, token consumption, latency, and agent performance at scale.
  • Experience building evaluation frameworks and measurement systems for context quality, retrieval effectiveness, hallucination reduction, and agent productivity.
  • Background in developer experience, platform engineering, internal developer tooling, or enterprise AI enablement platforms.
  • Experience operating in large enterprise environments with compliance requirements, governance controls, security review processes, and human oversight requirements.
  • Comfort operating in ambiguity and defining new technical patterns and platform capabilities where industry standards are still evolving.

About the Company

Dell Technologies

Makes PCs, AI servers and data storage systems

Dell Technologies makes laptops, desktops, servers, data storage and networking equipment for businesses, governments, schools and consumers, and it builds many of the server racks inside large AI data centers. In 2016 it completed its $67 billion purchase of storage maker EMC, then the largest technology deal ever. One division sells PCs, monitors and Alienware gaming computers, while the other sells PowerEdge servers, storage arrays and networking gear to companies running their own data centers. Dell earns money from those hardware sales plus support, financing and subscription services. Michael Dell founded the company in 1984 from his University of Texas dorm room, and it is based in Round Rock, Texas.

Company size
10,001+
Company stage
IPO
Headquarters
Round Rock, TX
Founded
1984

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