Architect

Brillio

. Define agentic AI reference architectures across LLM applications, RAG pipelines, tool/function calling, memory systems, multi-agent orchestration, and enterprise integrations .

Posted 9/24/2026full-timeRemote • New York • United StatesSeniorLead💰 $160,000 - $165,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and deploying agentic AI solutions across various enterprise functions, with a strong focus on LLM applications, AI workflow orchestration, and integration with cloud platforms. Proficient in leveraging modern engineering practices to drive operational efficiency and measurable business value.

Highest-signal resume keywords

AI Application DevelopmentLLM UnderstandingPython ProgrammingCloud Platform ExperienceAI Workflow Orchestration

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringSolution ArchitectureAI FrameworksPrompt EngineeringVector DatabasesMicroservicesSQL/NoSQLEvent-Driven SystemsProduction DeploymentModel Optimization

Tools & Technologies

LangChainGoogle CloudAWSAzureDockerKubernetesCI/CDDevSecOpsGoogle Workspace APIsSlack Integrations

Industry Keywords

Agentic AIRAG PipelinesMulti-Agent OrchestrationEnterprise IntegrationsObservability ToolsFeedback LoopsAutonomous WorkflowsAI-Driven SDLCEnterprise AutomationOpen-Source LLMs

Tech Stack

Tools & technologies

AWSAzureCloudDockerJavaJavaScriptKubernetesMicroservicesNode.jsNoSQLPythonReactSDLCSQLTypeScriptGo

About the role

Key responsibilities & impact

Define agentic AI reference architectures across LLM applications, RAG pipelines, tool/function calling, memory systems, multi-agent orchestration, and enterprise integrations
Design and deploy AI agents and autonomous workflows for Finance, Legal, Operations, Sales, Support, and Growth functions
Evaluate foundation models and enterprise AI platforms including Gemini, Vertex AI, OpenAI, Anthropic, Azure OpenAI, AWS Bedrock, and open-source LLMs
Integrate AI solutions with Google Workspace, Slack, CRM systems, internal APIs, databases, and knowledge repositories
Implement observability, evaluation, guardrails, monitoring, reliability controls, and responsible AI practices for production applications
Enable clients to move from AI experimentation to secure, scalable, production-grade agentic AI solutions
Accelerate enterprise automation, developer productivity, operational efficiency, and time-to-market through reusable AI frameworks and modern engineering practices
Drive measurable business value through AI-first platforms aligned to customer success and engineering excellence

Requirements

What you’ll need

8+ years of software engineering, solution architecture, enterprise architecture, or platform engineering experience
2+ years of hands-on experience building AI, GenAI, LLM-powered, or agentic applications
Strong understanding of LLMs, RAG, prompt engineering, vector databases, tool/function calling, context management, memory systems, and AI workflow orchestration
Hands-on experience with LangChain, LangGraph, CrewAI, Google ADK, AutoGen, Semantic Kernel, or similar frameworks
Strong engineering skills in Python and one or more of Java, Go, Node.js, React, TypeScript, APIs, microservices, SQL/NoSQL, and event-driven systems
Experience with Google Cloud, AWS, or Azure, including Docker, Kubernetes, CI/CD, DevSecOps, and cloud-native deployment patterns
Production deployment experience preferred
Experience with AI evaluation frameworks, observability tools, guardrails, and feedback loops is nice to have
Knowledge of fine-tuning, model optimization, open-source LLM deployment, multi-agent coordination, and autonomous decision-making systems is nice to have
Experience with Pinecone, Weaviate, Chroma, Vertex AI Vector Search, Google Workspace APIs, Slack integrations, or enterprise automation tools is nice to have
Exposure to AI-driven SDLC tools, cloud certifications, open-source AI contributions, or fast-paced innovation environments is nice to have