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AI Systems Engineer (LLM & AI Agent Development) Position Type: Full-Time, Remote Working Hours: U.S. Business Hours About the Role We are looking for a highly technical AI Systems Engineer to help build, maintain, and expand a growing ecosystem of AI-powered applications, autonomous agents, and internal software systems. This is an execution-focused engineering role. You will work directly with the founder to implement AI architectures, build new AI-driven features, improve existing systems, and rapidly deliver production-ready solutions. We are specifically looking for someone who has built sophisticated AI systems in production—not someone who has simply used ChatGPT or experimented with prompt engineering. Responsibilities AI Systems Development Build and maintain AI-powered web applications. Develop and improve autonomous AI agent systems. Implement multi-agent workflows and orchestration. Build AI systems where multiple agents coordinate under a master orchestrator. Develop recursive AI workflows and advanced automation pipelines. Integrate LLMs into production applications and internal tools. Product & Platform Development Expand and maintain an internally developed CRM platform. Build new features across multiple AI-powered products. Improve production systems through debugging, optimization, and ongoing enhancements. Build scalable backend services and APIs. Collaborate directly with the founder to rapidly execute product ideas. Integrations & Automation Integrate AI applications with HubSpot and other business systems. Build API integrations between internal platforms and third-party applications. Improve workflow automation and data synchronization across multiple systems. Support AI-powered business assistants and internal automation tools. AI Architecture & Agent Ecosystems Build and maintain graph memory architectures. Design secure information-sharing between AI agents. Implement memory management and data segmentation strategies. Ensure privacy controls and security boundaries across AI systems. Optimize AI performance, reliability, and scalability. Required Experience & Skills Must-Have 5+ years of professional software engineering experience. Strong experience building production AI applications. Proven experience developing autonomous AI agent systems. Experience implementing multi-agent orchestration and AI workflows. Strong Python development experience. Strong Node.js development experience. Working knowledge of Java. Experience integrating Large Language Models (LLMs) into production software. Strong prompt engineering experience for production environments. Experience building AI systems with graph memory and agent memory management. Experience designing secure AI architectures with data segmentation and privacy controls. Experience integrating APIs and business platforms. Hands-on experience integrating HubSpot or Salesforce into custom applications. Strong understanding of modern web application development. Experience using: Claude Code Cursor Replit OpenClaw (or equivalent AI engineering frameworks) Excellent written and spoken English communication skills. Ability to work independently with minimal supervision. What Makes You a Strong Fit You have built sophisticated AI systems that are running in production. You enjoy implementing complex technical solutions quickly and accurately. You are comfortable working directly with a founder and turning ideas into working software. You proactively solve problems without waiting for detailed specifications. You thrive in fast-moving startup environments. You communicate clearly and are comfortable discussing technical topics live with stakeholders. What Does a Typical Day Look Like? You'll spend most of your day building and improving AI-powered applications, expanding autonomous agent workflows, integrating business systems, and implementing new features across multiple products. You'll work closely with the founder to execute technical requirements, optimize production systems, build new AI capabilities, and continuously improve the company's AI ecosystem. Success in this role comes from delivering reliable, scalable AI systems that help automate business operations and accelerate product development. Interview Process Initial Recruiter Screening Live Technical Interview Offer Stage What Happens After You Apply Right after you apply, you'll receive an email invitation from Spark Hire to record your Intro Video. It's a short, self-recorded video completed on your own time and is the final step that completes your application. Instead of repeating yourself across multiple screening calls, you'll have one opportunity to introduce yourself and showcase your communication skills. Hiring managers review your video before scheduling interviews, making the hiring process faster and more efficient. Please watch for the invitation from Spark Hire in both your inbox and spam folder. #LI-AG1
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.