GenAI Software Development Architect

THE ROLE: 

We are building an AI-native hardware and firmware validation platform from the ground up — one where LLMs, RAG pipelines, autonomous agents, and knowledge graphs are the core of how the system works, not an add-on. As the Software Development Architect, you will own the end-to-end technical design of this platform: multi-agent orchestration, retrieval-augmented knowledge systems, MCP server infrastructure, and the engineering standards that make all of it reliable at scale. This role sits within the Global Cluster Engineering organization, where you will develop software that powers distributed infrastructure at global scale. You will work closely with validation engineers, hardware teams, and leadership to translate domain requirements into a production-grade AI-native system. This is a hands-on role — you will write code, drive technology decisions, and directly mentor engineers.

THE PERSON: 

• Experience:  software development experience, with at least 4 years in architecture, staff, or principal engineer role
• AI-Native Systems: Deep, hands-on experience designing and shipping production AI-native systems — not just LLM API integration, but the full stack: RAG pipelines, agent orchestration, tool use, multi-agent coordination, and LLM evaluation
• LLM Fundamentals: Strong understanding of how LLMs work in practice — context windows, grounding, hallucination failure modes, prompt engineering, model selection, and how behavior changes across providers and versions
• Retrieval Systems: Proven experience with vector search, embedding models, hybrid retrieval, reranking pipelines, and knowledge graph-augmented RAG
• Core Skills: Strong proficiency in one or more modern programming languages such as Python, TypeScript/Node.js, Go, Java, C#, or Rust, with demonstrated ability to build and operate production-scale services. Python experience is preferred due to the AI/ML ecosystem
• Engineering excellence: Async programming, API design, distributed systems, clean code practices. Experience designing for reliability in automated/unattended environments — crash recovery, audit trails, state management, observability. Strong written communication — architecture docs, design specs, and engineering standards that outlast your tenure. Track record of setting engineering standards that teams follow
• Hardware Affinity: Experience working closely with hardware teams — servers, networking equipment, or compute infrastructure — with an understanding of how software interacts with physical systems
• Cloud Infrastructure: Experience with AWS, Azure, or GCP — infrastructure provisioning, managed services, networking, and deploying production workloads at scale
• AI Tooling: Demonstrated use of AI coding assistants and LLM-powered developer tools (Claude Code, GitHub Copilot, Cursor, etc.) to accelerate design, development, and documentation

KEY RESPONSIBILITIES: 

• Platform Architecture: Design and own the architecture of an AI-native validation platform where autonomous LLM agents plan, execute, and analyze hardware and firmware test campaigns end-to-end — without a human in the loop
• RAG System Design: Architect the full retrieval-augmented generation stack — document ingestion pipelines, chunking strategies, embedding models, vector stores, knowledge graph backends, hybrid search, cross-encoder and LLM-based reranking — ensuring agents have accurate, grounded knowledge at query time
• Agent Orchestration: Define multi-agent dispatch patterns, context window management strategies, anti-hallucination contracts, tool-use boundaries, inter-agent communication protocols, and crash recovery mechanisms for long-running unattended runs
• MCP Integration: Own the integration architecture between the agent layer (Claude Code / Model Context Protocol), the knowledge backend (Qdrant, Neo4j / LightRAG), and external systems (Slack, Jira, Confluence, GitHub) or equivalent
• Engineering Standards: Establish and enforce AI-native development standards — prompt design, skill authoring, agent contract specifications, artifact schemas, and evaluation methodology for LLM outputs
• LLM Reliability: Lead the team's approach to building trustworthy agentic systems — fabrication detection, context compaction recovery, output validation, and post-run audit infrastructure
• Technology Evaluation: Continuously evaluate new LLM capabilities, model releases, embedding models, and agentic frameworks; make pragmatic adoption decisions
• AI Services: Design and implement scalable, low-latency AI services powering metadata generation, feature extraction, and knowledge retrieval across the validation platform
• Agentic AI Deployment: Develop and deploy agentic AI solutions — autonomous agents, multi-agent orchestration frameworks, and LLM-powered workflows — that transform hardware validation, firmware QA, and lab operations
• Stakeholder Collaboration: Partner with engineering peers, validation engineers, and business stakeholders to understand requirements and translate them into flexible, future-proof design solutions
• Security & Compliance: Ensure AI/ML systems comply with security standards and best practices, addressing data privacy and protection concerns across all LLM integrations and knowledge pipelines
• End-to-End Ownership: Own the platform end-to-end — from project estimation and architecture review through coding, deployment, and post-launch measurement
• Operational Excellence: Build resilient systems with strong observability; establish automated testing, monitoring, and CI/CD pipelines using infrastructure-as-code tools (Terraform); lead root-cause analysis and drive continuous reliability improvements
• Team Leadership: Mentor software developers, conduct design reviews, and set the technical bar for the team

PREFERRED EXPERIENCE: 

• Background in the semiconductor or datacenter industry — hardware validation, firmware development, or silicon bring-up
• Experience with network hardware (NICs, switches, GPUs) or associated diagnostics (PCIe, RDMA, etc)
• Familiarity with the Model Context Protocol (MCP) or agentic platforms (LangGraph, CrewAI, AutoGen)
• Published work, open source contributions, or talks in the AI/LLM space
• Data Engineering & Analytics: Experience with data pipeline design, ETL workflows, data warehousing, or analytics platforms is a plus

ACADEMIC CREDENTIALS: 

BS or MS Degree in Computer Science, Electrical Engineering, or related field

LOCATION: 

Santa Clara 

Austin or Seattle or Secaucus

THE ROLE: 

We are building an AI-native hardware and firmware validation platform from the ground up — one where LLMs, RAG pipelines, autonomous agents, and knowledge graphs are the core of how the system works, not an add-on. As the Software Development Architect, you will own the end-to-end technical design of this platform: multi-agent orchestration, retrieval-augmented knowledge systems, MCP server infrastructure, and the engineering standards that make all of it reliable at scale. This role sits within the Global Cluster Engineering organization, where you will develop software that powers distributed infrastructure at global scale. You will work closely with validation engineers, hardware teams, and leadership to translate domain requirements into a production-grade AI-native system. This is a hands-on role — you will write code, drive technology decisions, and directly mentor engineers.

THE PERSON: 

• Experience:  software development experience, with at least 4 years in architecture, staff, or principal engineer role
• AI-Native Systems: Deep, hands-on experience designing and shipping production AI-native systems — not just LLM API integration, but the full stack: RAG pipelines, agent orchestration, tool use, multi-agent coordination, and LLM evaluation
• LLM Fundamentals: Strong understanding of how LLMs work in practice — context windows, grounding, hallucination failure modes, prompt engineering, model selection, and how behavior changes across providers and versions
• Retrieval Systems: Proven experience with vector search, embedding models, hybrid retrieval, reranking pipelines, and knowledge graph-augmented RAG
• Core Skills: Strong proficiency in one or more modern programming languages such as Python, TypeScript/Node.js, Go, Java, C#, or Rust, with demonstrated ability to build and operate production-scale services. Python experience is preferred due to the AI/ML ecosystem
• Engineering excellence: Async programming, API design, distributed systems, clean code practices. Experience designing for reliability in automated/unattended environments — crash recovery, audit trails, state management, observability. Strong written communication — architecture docs, design specs, and engineering standards that outlast your tenure. Track record of setting engineering standards that teams follow
• Hardware Affinity: Experience working closely with hardware teams — servers, networking equipment, or compute infrastructure — with an understanding of how software interacts with physical systems
• Cloud Infrastructure: Experience with AWS, Azure, or GCP — infrastructure provisioning, managed services, networking, and deploying production workloads at scale
• AI Tooling: Demonstrated use of AI coding assistants and LLM-powered developer tools (Claude Code, GitHub Copilot, Cursor, etc.) to accelerate design, development, and documentation

KEY RESPONSIBILITIES: 

• Platform Architecture: Design and own the architecture of an AI-native validation platform where autonomous LLM agents plan, execute, and analyze hardware and firmware test campaigns end-to-end — without a human in the loop
• RAG System Design: Architect the full retrieval-augmented generation stack — document ingestion pipelines, chunking strategies, embedding models, vector stores, knowledge graph backends, hybrid search, cross-encoder and LLM-based reranking — ensuring agents have accurate, grounded knowledge at query time
• Agent Orchestration: Define multi-agent dispatch patterns, context window management strategies, anti-hallucination contracts, tool-use boundaries, inter-agent communication protocols, and crash recovery mechanisms for long-running unattended runs
• MCP Integration: Own the integration architecture between the agent layer (Claude Code / Model Context Protocol), the knowledge backend (Qdrant, Neo4j / LightRAG), and external systems (Slack, Jira, Confluence, GitHub) or equivalent
• Engineering Standards: Establish and enforce AI-native development standards — prompt design, skill authoring, agent contract specifications, artifact schemas, and evaluation methodology for LLM outputs
• LLM Reliability: Lead the team's approach to building trustworthy agentic systems — fabrication detection, context compaction recovery, output validation, and post-run audit infrastructure
• Technology Evaluation: Continuously evaluate new LLM capabilities, model releases, embedding models, and agentic frameworks; make pragmatic adoption decisions
• AI Services: Design and implement scalable, low-latency AI services powering metadata generation, feature extraction, and knowledge retrieval across the validation platform
• Agentic AI Deployment: Develop and deploy agentic AI solutions — autonomous agents, multi-agent orchestration frameworks, and LLM-powered workflows — that transform hardware validation, firmware QA, and lab operations
• Stakeholder Collaboration: Partner with engineering peers, validation engineers, and business stakeholders to understand requirements and translate them into flexible, future-proof design solutions
• Security & Compliance: Ensure AI/ML systems comply with security standards and best practices, addressing data privacy and protection concerns across all LLM integrations and knowledge pipelines
• End-to-End Ownership: Own the platform end-to-end — from project estimation and architecture review through coding, deployment, and post-launch measurement
• Operational Excellence: Build resilient systems with strong observability; establish automated testing, monitoring, and CI/CD pipelines using infrastructure-as-code tools (Terraform); lead root-cause analysis and drive continuous reliability improvements
• Team Leadership: Mentor software developers, conduct design reviews, and set the technical bar for the team

PREFERRED EXPERIENCE: 

• Background in the semiconductor or datacenter industry — hardware validation, firmware development, or silicon bring-up
• Experience with network hardware (NICs, switches, GPUs) or associated diagnostics (PCIe, RDMA, etc)
• Familiarity with the Model Context Protocol (MCP) or agentic platforms (LangGraph, CrewAI, AutoGen)
• Published work, open source contributions, or talks in the AI/LLM space
• Data Engineering & Analytics: Experience with data pipeline design, ETL workflows, data warehousing, or analytics platforms is a plus

ACADEMIC CREDENTIALS: 

BS or MS Degree in Computer Science, Electrical Engineering, or related field

LOCATION: 

Santa Clara 

Austin or Seattle or Secaucus