The AI Systems Developer is a senior, multidisciplinary technology role responsible for the end-to-end ownership of enterprise-grade software solutions, from concept through production and long-term maintenance.
Compensation
$90,000 – 95,000 annually, commensurate with education and experience.
Responsibilities
Translate high-level business concepts and stakeholder goals into clear technical requirements
Define MVP scope versus enterprise-level requirements
Identify user personas, workflows, and success criteria
Design scalable, resilient, and maintainable system architectures
Select appropriate technology stacks, frameworks, databases, and APIs
Build high quality, robust, efficient, and scalable analytical solutions that directly support portfolio construction, risk management, and alpha generation for the Fixed Income Embedded Quant Development Team.
Responsibilities
Gather ideas and conceptualize them through a programming language
Create a packaged solution usable by investment professionals
Support portfolio construction, risk management, and alpha generation
Requirements
Bachelor’s degree in a quantitative field such as Financial Engineering, Physics, Applied Mathematics, Computer Science, or a closely related discipline
Technical Solutions Engineers understand the fundamentals of programming incredibly well and eagerly familiarize themselves with others’ code. You’ll explain technical concepts about Stripe’s products, build and debug integrations with developers, and produce and improve guides that help our users solve problems and succeed with Stripe.
Responsibilities
Communicate with external developers and help them build applications using Stripe’s infrastructure across programming languages and frameworks, including Java, Python, Ruby, PHP, Go, Node.js, .NET, React, React Native, Swift, Objective-C, Kotlin, and Java for Android
Unblock external developers facing complex integration challenges through email, ticketing software, issue trackers, developer community platforms, and real-time chat
Develop and maintain internal and external documentation, examples, integration demos, and code snippets that demonstrate usage patterns for Stripe’s infrastructure
Identify points of friction developers encounter and collaborate with engineering teams to improve Stripe products, including participating in their regular planning sessions, design reviews, and development meetings
Develop and maintain internal support tools for TSE and Stripe Support more broadly, such as a retrieval-augmented generation copilot powered by a large language model or a tool that explains invoices
Requirements
degree in computer science
degree in software engineering
degree in computer engineering
Forward Deployed Engineer, GenAI, Youtube Go-To-Market Technology and Insights
Join the AI Accelerator team to drive AI transformation for YouTube Business Go-To-Market (GTM) operations, operating as a high-velocity, horizontal transformation engine at the intersection of consulting, product strategy, applied AI, and systems engineering.
Partner with stakeholders to co-create tool roadmaps, translating ambiguous business problems into tangible software solutions that enable YouTube's operations.
Iterate GenAI proof-of-concepts (LLMs, RAG, agentic frameworks) into production-grade AI applications (multi-agent systems, MCP servers) that drive measurable ROI.
Shape and lead the development of a next-generation AI product ecosystem for business operations, owning the architecture and technical direction of production-grade agentic systems.
Compensation
Competitive annual salary: $200,000–$300,000, depending on experience, skills, and qualifications.
Responsibilities
Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.
Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.
Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.
Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.
Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.
Shape and lead the development of a next-generation AI product ecosystem for business operations, owning the architecture and technical direction of production-grade agentic systems.
Compensation
Competitive annual salary: $200,000–$300,000, depending on experience, skills, and qualifications.
Responsibilities
Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.
Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.
Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.
Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.
Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.
Shape and lead the development of a next-generation AI product ecosystem for business operations, owning the architecture and technical direction of production-grade agentic systems.
Compensation
Competitive annual salary: $200,000–$300,000, depending on experience, skills, and qualifications.
Responsibilities
Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.
Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.
Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.
Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.
Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.
Shape and lead the development of a next-generation AI product ecosystem for business operations, owning the architecture and technical direction of production-grade agentic systems.
Compensation
Competitive annual salary: $200,000–$300,000, depending on experience, skills, and qualifications.
Responsibilities
Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.
Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.
Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.
Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.
Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.
Shape and lead the development of a next-generation AI product ecosystem for business operations, owning the architecture and technical direction of production-grade agentic systems.
Compensation
Competitive annual salary: $200,000–$300,000, depending on experience, skills, and qualifications.
Responsibilities
Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.
Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.
Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.
Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.
Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.
Shape and lead the development of a next-generation AI product ecosystem for business operations, owning the architecture and technical direction of production-grade agentic systems.
Compensation
Competitive annual salary: $200,000–$300,000, depending on experience, skills, and qualifications.
Responsibilities
Lead the architecture, design, and development of a production-grade multi-agent system capable of safely executing actions across core business tools, including ERP, CRM, spreadsheets, calendars, email, analytics, and compliance platforms.
Own the development and continuous improvement of Model Context Protocol (MCP) integrations that connect internal business systems with external AI environments and assistants.
Design and implement a unified data infrastructure that consolidates information from multiple sources into a centralized layer accessible to AI agents and external tools.
Build contextual memory and customer profiling systems that ingest business data and enable increasingly personalized, intelligent AI experiences.
Establish robust reliability, observability, permission management, and durable execution mechanisms to ensure agents operate safely on real customer data.
Execute the full software development lifecycle: author technical design documents, ship maintainable code, build intuitive frontend surfaces, and manage deployment.
Build high-performance evaluation (eval) pipelines and observability frameworks to ensure agentic systems meet accuracy, safety, compliance, and latency requirements.
Resolve technical hurdles preventing AI maturity, including data readiness gaps, system integration complexities, edge-case managing, and state-management issues.
AI Systems DeveloperNewUniversity of Florida · Gainesville, US
Technical Solutions EngineerNewStripe · Remote, Singapore
Forward Deployed Engineer, GenAI, Youtube Go-To-Market Technology and InsightsNewGoogle · San Bruno, US+2 more
Staff AI Product EngineerNewJobgether · Remote, India