Senior Software Developer - AI Agents & Systems Design
Valsoft Corporation
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Benefits offered by this job
Job summary
ServiceCentral is seeking a senior software engineer who makes AI a structural part of software—not as features, but as engineering fundamentals. You’ll build with AI agents, MCPs, and knowledge pipelines across the full SDLC, from architecture drafting to deployment.
Lead end-to-end architecture decisions, design scalable SaaS solutions, and ensure reliability and observability while integrating AI tooling deeply into the development workflow.
Qualifications
- Senior-level software development experience with end-to-end architecture ownership.
- Hands-on production experience building with AI agents for multi-step tasks.
- Experience designing/consuming MCPs to connect AI to tools, APIs and data sources.
- Experience building knowledge repositories (RAG pipelines, vector DBs, embeddings).
- Experience using AI coding tools as part of the development workflow.
- Strong backend skills and shipping production SaaS applications.
- Experience designing APIs, data models, and service integrations.
Responsibilities
- Design, develop, and maintain production-grade SaaS apps using AI agents as core tooling.
- Own architecture decisions end-to-end and document tradeoffs for maintainability.
- Translate operational needs into well-architected software solutions.
Skills
Tools
Job description
ServiceCentral builds software for the device repair and reverse logistics industry — point-of-sale, enterprise service management, and repair network tools used by 500+ customers across 100+ countries. Customers include national insurance carriers, OEM-authorised service providers, and franchise repair networks.
We are looking for a senior software developer who has made AI a structural part of how they build software — applying agentic systems, knowledge pipelines, and AI-connected tooling not as features, but as engineering fundamentals.
This is not a role for someone who has added AI features to an app. We're looking for an experienced engineer who has restructured how software gets built — using AI agents to automate development tasks, MCPs to connect AI systems to real tools and data, and knowledge repositories to give AI systems reliable, domain-specific grounding.
Strong software architecture and system design are the baseline. AI tool mastery is the differentiator. This role sits at the intersection of both, and neither substitutes for the other.
Design, develop, and maintain production-grade SaaS applications using AI agents and tooling as first-class parts of your workflow, not bolt-ons. Use agentic systems to automate development tasks: scaffolding, refactoring, test generation, code review, documentation. Build and maintain MCP servers that connect AI systems to codebases, APIs, databases, and internal tools so AI assistants have reliable, scoped access to the context they need. Design and maintain knowledge repositories (vector databases, retrieval pipelines, embeddings) that give AI systems accurate, current, domain-specific grounding. Apply these tools across the full SDLC: from architecture drafting and requirements analysis through implementation, testing, and deployment.
Own architecture decisions end-to-end. Design service boundaries, API contracts, data models, and integration patterns with long-term maintainability in mind. Reason through tradeoffs explicitly and document them so others can understand the reasoning, not just the outcome. Design for observability, reliability, and failure from the start. Identify and address structural technical debt before it compounds.
Design, develop, and maintain scalable line-of-business SaaS applications. Build backend services, APIs, and data models. Ensure system performance, reliability, and security. Translate operational business needs into well-architected software solutions.
- Senior-level software development experience: you have owned architecture decisions, designed systems from scratch, and can articulate the tradeoffs you made and what you'd change
- Proven, hands-on experience building with AI agents in production: systems that use LLMs to plan, use tools, and execute multi-step tasks reliably
- Practical experience building or consuming MCPs (Model Context Protocol) to connect AI models to tools, APIs, codebases, or data sources
- Experience designing and maintaining knowledge repositories (RAG pipelines, vector databases, embedding strategies) that provide reliable grounding for AI systems
- Experience using AI coding tools (e.g. Cursor, Claude Code, GitHub Copilot) as a structured part of your development workflow, not just for autocomplete
- Strong backend development skills and experience shipping production SaaS applications
- Experience designing APIs, data models, and service integrations that other developers build on
- Experience with PHP or .NET development stacks
- Experience with MySQL or Oracle database engines
- Experience with distributed systems and message queues
- Experience building or maintaining vertical SaaS or B2B products
- Experience working in small, high-ownership teams
- Use AI tooling at the level it's actually capable of, not as a novelty or an experiment
- Own architecture decisions from day one with real influence over technical direction
- Build systems real businesses depend on, not demos or proofs of concept
- Room to grow as the platform expands around AI-driven automation
ServiceCentral builds the software that runs product service after the sale: warranty claims, repair authorization, and service execution, for everyone from the local repair shop to the Fortune 50 OEM. We are backed by Valsoft Corporation, a permanent-capital owner of 150+ vertical software companies, so we build for the next decade, not the next funding round.
- Senior-level software development experience: you have owned architecture decisions, designed systems from scratch, and can articulate the tradeoffs you made and what you'd change
- Proven, hands-on experience building with AI agents in production: systems that use LLMs to plan, use tools, and execute multi-step tasks reliably
- Practical experience building or consuming MCPs (Model Context Protocol) to connect AI models to tools, APIs, codebases, or data sources
- Experience designing and maintaining knowledge repositories (RAG pipelines, vector databases, embedding strategies) that provide reliable grounding for AI systems
- Experience using AI coding tools (e.g. Cursor, Claude Code, GitHub Copilot) as a structured part of your development workflow, not just for autocomplete
- Strong backend development skills and experience shipping production SaaS applications
- Experience designing APIs, data models, and service integrations that other developers build on
- Remote work
- Competitive salary (salary range of $130k-$150k CAD)
- Bonus structure based on performance.
- Health & benefits plan available
- Unlimited flexible paid time off
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