Note: The job is a remote job and is open to candidates in USA. IgniteTech is reinventing how community engagement and social media management software is built by integrating AI at its core. The Software Engineering Superbuilder role focuses on developing systems that empower AI to autonomously manage workflows and enhance team productivity while ensuring high-quality software delivery for enterprise clients.
Responsibilities
- Build and continuously sharpen the system that lets AI own the work — the tools, skills, context, rules, and self-verifying harnesses agents need to run whole end-to-end workflows, so each class of work needs less human intervention over time. This is the heart of the role
- Direct and improve a fleet of agents: delegate units of work in parallel, quality-check what comes back, and when an agent falls short, fix the underlying capability rather than just the output
- Ship enterprise-grade, AI-powered features in a tight closed loop with product leadership — from reviewed plan and spec, through agent execution, to merged production releases. Fast enough that product and customer feedback can be implemented within the day
- Multiply the whole team, durably: share your fixes back as reusable rules, context, and skills — so everyone's ceiling rises, not just yours
- Lead a senior team of engineers — scoping work, reviewing approaches and PRs against the system model, and holding the quality bar before anything merges
Skills
- AI-DNA — it's how you operate, not a tool you reach for. You don't write code by hand or sit in 1:1 loops with an assistant. Your default is to get out of the AI's way: build the context, tools, and guardrails that let agents own work end-to-end, then direct and verify
- Strong product taste. You see a problem and figure out a good way to solve it without a detailed spec — a thinker and proposer, not just an implementer. Your output is polished and user-considered, with few bugs, so product leadership can hand you a goal and trust what comes back
- Bottleneck-removal as your default. You spend more time building what agents need to work autonomously — skills, rules, context, harnesses, evals — than hand-driving tasks, and you operate closed-loop and in parallel, not 1:1. Tokens and tools are never the constraint here — your leverage through them is. Bluntly: if you're not efficiently spending your salary in tokens — turning a large budget into shipped, high-quality enterprise software and durable agent capability — you're not operating at the level this role demands
- Frontier tool fluency, deep and broad. You command today's best agentic coding tools at a power-user level and manage fleets of agents — and you're never locked to one. You constantly test alternatives to spot the next best tool early. Mastery of one tool with no curiosity about the rest is a red flag; so is never having gone deep on any
- Sharp, current model judgment. You can argue which frontier model to use when and why, what each is better at, what changed across recent releases, and what each provider just shipped. You start with the most capable model, then optimize for cost and latency
- Ownership & proactiveness. Mission-driven, with a history of obsessing over the quality of your work, and no babysitting required — even on the boring tasks. You move work forward, surface risk early, and overcome blockers rather than escalate them
- 5+ years of full-stack software engineering, primarily on web-based systems — genuine front-end and back-end depth, by a senior engineer who's already had a strong IC career and stepped up as a tech lead
- Production AI/LLM integration — you've shipped real applications using LLMs via APIs, prompt engineering, agents, or automation
- Cloud & delivery (one-stop-shop) — solid AWS and CI/CD experience, enough to own a product end-to-end as a one-person band: stand it up, ship it, and operate it without handing off the infrastructure
- Model Context Protocol (MCP) — demonstrated understanding and prior application
- Technical leadership & complex decisions — you've made hard architectural calls, led PR review, and directed senior and mid-level engineers; you set direction and hold the bar, not just produce
- Enterprise-grade software experience — you've shipped production software at enterprise quality. Defensive coding, edge cases, validation, security, resilience, and automated evals are your default. You don't ship open-loop AI systems
- Prior experience building on enterprise community, social messaging, or customer-engagement platforms
- Published writing, talks, or open-source contributions in AI-assisted engineering, agentic systems, or full-stack/LLM product work
- Experience designing multi-agent or orchestrator/sub-agent workflows (parallel execution, worktrees, file-based agent communication)
- A track record of building reusable internal tooling — shared skills, rules, eval frameworks — that measurably raised a whole team's output
Benefits
- Fully remote, async-first, global. No office.
Company Overview