Staff AI Architect

Robots & Pencils

. Define AI system architecture and lead design across LLM applications, agentic systems, retrieval, and ML model serving for end-to-end engagements .

Posted 9/29/2026full-timeRemote • United StatesLead💰 $146,071 - $201,541 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates deep expertise in AI system architecture, particularly in LLM and agentic applications, with a strong focus on model serving and inference optimization on AWS. Proven ability to lead technical teams, establish architectural standards, and communicate complex AI concepts effectively.

Highest-signal resume keywords

AI/ML ArchitectureModel Serving Optimization on AWSLLMOps and MLOps StandardsMulti-Agent Orchestration DesignResponsible AI Governance

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

PythonTypeScriptJavaGoRAG Systems DesignHybrid RetrievalModel Fine-TuningCloud-Native ArchitectureMicroservicesContainerization

Soft Skills

Problem-SolvingTechnical MentoringCommunication

Tools & Technologies

LangGraphAmazon Bedrock AgentsAgentCoreClaudeCursor

Certifications & Qualifications

AWS AI Certifications

Industry Keywords

AI EvaluationData IsolationPrompt-Injection DefenseObservabilityCI/CD

Tech Stack

Tools & technologies

AWSCloudJavaMicroservicesPythonTypeScriptGo

About the role

Key responsibilities & impact

Define AI system architecture and lead design across LLM applications, agentic systems, retrieval, and ML model serving for end-to-end engagements
Drive RAG and context-engineering strategy, including hybrid retrieval, reranking, chunking, and embedding model selection across vector stores
Lead multi-agent orchestration design using frameworks such as LangGraph, Amazon Bedrock Agents, or AgentCore
Own model serving and inference architecture on AWS, optimizing latency, throughput, and cost with streaming, caching, batching, and quantization
Define LLMOps and MLOps standards across engagements, including prompt versioning, evaluation pipelines, CI/CD, and feature stores
Drive evaluation, guardrails, and observability strategy, establishing offline and online evaluation, LLM-as-judge scoring, and tracing
Own responsible AI posture, including governance, PII and data isolation, prompt-injection defense, and grounding controls
Lead decisions on fine-tuning, prompting, RAG, and tool use while weighing accuracy, cost, and maintainability
Use AI-forward tools such as Claude and Cursor to ship higher-quality work at pace
Partner with leadership and clients on AI technical direction and translate business goals into architectural decisions
Communicate complex AI concepts and tradeoffs to engineering and non-engineering stakeholders
Collaborate with engineering, data, ML, and product teams
Develop and maintain AI architecture documentation, evaluation standards, and reusable patterns
Establish architectural standards and best practices across the team
Mentor junior and mid-level engineers
Act as a technical escalation point for complex architectural and integration challenges
Evaluate emerging technologies and recommend tools, frameworks, and patterns

Requirements

What you’ll need

7+ years of professional software engineering experience
At least 3 years in AI/ML architecture or technical leadership roles
Deep expertise in LLM and agentic application architecture, including multi-agent orchestration and tool-calling patterns
Strong experience designing production RAG and retrieval systems, including hybrid retrieval, reranking, and vector store selection
Strong experience with model serving and inference optimization on AWS, including latency, throughput, and cost tuning
Deep understanding of LLMOps and MLOps, including eval pipelines, prompt versioning, CI/CD, and observability for AI workloads
Demonstrated experience with AI evaluation, guardrails, and tracing tooling
Strong judgment on fine-tuning versus prompting versus RAG tradeoffs for accuracy, cost, and maintainability
Expert software engineering background, with proficiency in Python and at least one other language (e.g., TypeScript, Java, Go)
Strong background in cloud-native architecture, including microservices, serverless, containerization, and event-driven systems
Strong understanding of responsible AI and governance, including data isolation, prompt-injection defense, and compliance
Demonstrated leadership and technical mentoring experience across a team
Demonstrable, day-to-day usage and expert knowledge of AI-forward tools such as Claude and Cursor
Excellent problem-solving skills and ability to navigate ambiguous technical and business challenges
AWS AI certifications, fine-tuning or model customization experience, or responsible AI experience is a plus
Successful completion of a background check may be required

Benefits

Comp & perks

Paid time off
Medical insurance
Dental insurance
Vision insurance
401(k)