Job Summary: ServiceMinder is seeking a Staff Software Engineer to rebuild its franchise platform, integrating AI directly into the product and establishing the technical direction and engineering standards for the team. Key Highlights: 1. Build software for hundreds of franchise businesses using AI. 2. Define the technical direction of the web platform (frontend and backend). 3. Staff role with direct impact on architecture and tools. **ServiceMinder** is the operating system for franchise businesses across North America. We are rebuilding our platform with **AI integrated directly into the product**, not as an afterthought tacked on at the end, but as part of how the system fundamentally works. We are hiring a **Staff Software Engineer** who builds production-ready software, makes sound architectural decisions, and delivers AI capabilities that franchise operators actually use. You will be responsible for the technical direction of the entire web platform — frontend and backend — deciding **how and where AI fits within the product**, and setting the engineering standard for the team. You will build systems that **hundreds of franchise businesses rely on daily.** What You’ll Do Build and Design Architecture * Understand how franchise operators truly work — both inside and outside the platform — and identify where AI eliminates real friction, not just where it looks impressive in a demo. * Partner closely with the **Product** team to translate domain knowledge into functional AI. LLMs are only as good as the context provided: the person who understands **scheduling, dispatching, billing, and customer communication** will build better AI than someone who only understands transformers. * Design and deliver cloud-native systems using **TypeScript and C\#**, with **Python** for AI integration, evaluation, and tooling. Clear service boundaries, robust API contracts, and scalable data architecture. * Own the **end-to-end frontend experience**. Build fast, accessible, production-ready web interfaces using modern frameworks (**Angular, React or equivalents**). You care about UX, performance, and ensuring AI capabilities feel natural within the product — not artificially bolted on. * Build product features leveraging **LLMs, embeddings, and vector search** to automate tasks currently performed manually by franchise operators. * Make **build vs buy vs integrate** decisions for AI tools: when to call an external API, when to fine-tune a model, and when to build internally. * Own the integration layer between our core platform and AI services — model selection, prompt structure, information retrieval pipelines, and result evaluation. * Embed evaluation and measurement into AI features from day one — if you can’t measure whether a feature works, it shouldn’t ship. * Design corrective feedback loops in AI systems: observability, automated regression detection, result quality monitoring, and clear escalation paths when models fail or drift. Better brakes let us go faster. * Optimize database schemas, queries, and cloud deployments for performance and cost. Lead and Influence * Drive architectural decisions across the full stack — frontend architecture, API design, data modeling, service integration, and cloud infrastructure. * Lead technical discussions with a clear, evidence-backed point of view. Influence **roadmaps** and technology decisions. * Conduct deep code reviews with direct, actionable feedback on design, performance, and correctness. * Identify technical risks early — performance bottlenecks, security gaps, unreliable model outputs, and runaway inference costs. * Manage AI spend as an engineering discipline: model selection, provider routing, fallback strategies, and usage monitoring. At our scale, uncontrolled inference costs can erase the value of a feature. Evolve How the Team Works * You already use AI daily in development. You use tools like **Claude, Copilot, Cursor or similar** to write, review, refactor, and ship code faster. You don’t need to be told to try them — you’re already forming opinions on what works and what doesn’t. * Define how the team builds with AI: structured prompt design, result evaluation, failure handling, and cost management. * Automate the development cycle with AI — test model outputs, detect regressions before production, and make deployments routine. Manual QA for AI features does not scale. * Mentor engineers on both software engineering best practices and real-world AI integration — not just demos. * Evaluate new tools, frameworks, models, and providers. Know how to distinguish what’s genuinely useful from what’s overhyped. What We’re Looking For Must-Have Requirements * Strong **frontend** engineering skills. You’ve built and shipped complex, production-ready web applications using **Angular, React or modern equivalent frameworks**. You understand component architecture, state management, performance optimization, and accessibility. This is not a “backend engineer who writes some JSX” role. * Extensive professional experience building software in **TypeScript and C\#** (or Java or other strongly typed languages), plus practical fluency in **Python**. * Proven experience designing and building large-scale, **API- and service-based systems** — service decomposition, versioning, and integration patterns. * Strong **full-stack architecture** skills: designing scalable, resilient, and maintainable systems from database to browser. * Hands-on experience developing in the cloud (**Azure, AWS or equivalents**). * Deep expertise in **relational databases** — data modeling, query optimization, and schema evolution. * You take ownership of major technical deliverables **end-to-end and ship them**. * You currently use **AI in your engineering workflow**. It’s not just curiosity or future intent — you already use AI tools to write, review, debug, and ship code, and you have clear opinions on when they accelerate work and when they introduce risk. * You’ve shipped **AI/ML capabilities in production systems**: integrations with LLM APIs, embeddings and vector search, retrieval-augmented generation (RAG), or ML pipelines — not just proof-of-concepts. * You’ve built or operated systems with **production AI observability**: result quality monitoring, drift detection, and feedback loops. * Fluent English with strong written and verbal communication skills. In a remote environment, precise communication is essential for progress. You know how to explain technical decisions to non-technical stakeholders. * You treat the **Product** team as a partner — not a ticket queue. You bring technical options to the table and expect PMs to contribute business context. The best AI features emerge from that collaboration. Nice-to-Have * Experience in **home services, franchise systems, or field service management software**. * Experience with **payment processing, PCI compliance, or analytics integrations**. * Experience designing prompt structures, evaluating model outputs, or building retrieval pipelines. * Ability to use **SQL and data analysis** to measure whether AI features truly generate value. * Experience with **CI/CD, infrastructure-as-code, and production observability**, especially applied to AI deployment and model lifecycle management. * Experience building **high-traffic, customer-facing web applications**. Why ServiceMinder * **Impact:** Hundreds of franchise businesses run their operations on this platform daily. What you build impacts real businesses. * **AI that solves real problems:** We use AI to reduce manual work and surface information franchise operators need — not just to add chatbots to a page. * **Growth:** A high-growth SaaS company backed by a major private equity fund. * **Flexibility:** A **100% remote**, results-oriented culture. * **Influence:** The Staff role means you **define the technical direction**. You have a voice in architecture, tools, and what gets built next. Employment Type: Full-time Salary: S/.6,500\.00 \- S/.8,500\.00 per month Application Questions: * English speaking level: Basic, Intermediate, or Advanced * English writing level: Basic, Intermediate, or Advanced * English listening level: Basic, Intermediate, or Advanced * Have you implemented AI features in production (LLMs, embeddings, RAG, ML pipelines)? * Which frontend framework have you used most extensively in production? * How many years of experience do you have designing system architecture or leading technical decisions? * What are the main programming languages you’ve used professionally in production? * Please indicate the university you attended, your degree program, and year of graduation / degree completion Work Location: Remote
María García
Indeed · HR
María García
Indeed · HR
