AI Forward Engineer

AI-Forward Engineer This role blends the high-leverage AI-native builder, the broad-spectrum technologist who can cover wide ground through AI, and the ownership and “ship-it” instincts of a senior engineer who takes systems all the way into production. The role in one sentence You are a senior, AI-forward engineer who can operate across a broad technical surface, build internal systems companies run on, and turn each piece of work into reusable capability that compounds over time. Why this role exists We're building a small team of senior engineers who can deliver what a much larger team traditionally would, by using AI across the full surface of the work instead of staffing a specialist for every task. These aren't executors waiting on tickets. They're builders who can take an ambiguous internal problem, understand what actually needs to be solved, design the right system, direct agents and tools to help build it, and stay with it until it works reliably in production. They also treat every piece of work as an opportunity to make the next piece faster, safer, and better. What you'll own Taste and judgment. Producing plausible implementation is getting cheaper. Knowing what is actually correct, secure, maintainable, and fit for the situation is not. You can tell genuinely good work from plausible slop, and your judgment on what ships is the bar.Build with agents. You pair with coding agents on production software and increasingly review far more than you type. You are comfortable delegating implementation aggressively while remaining fully accountable for the architecture, quality, security, and production behaviour of what ships.Integration and automation. You architect and build the connections between systems and remove manual steps where automation creates real leverage. APIs, agent tooling, and protocols like MCP are part of the everyday technical surface.Business systems, not just features. You build the internal systems Fastloop runs on: connecting platforms, automating workflows, and creating AI-enabled capabilities used across the business. You own them end to end and stay with them until they work reliably in production.DevOps and infrastructure as code. You stand up and maintain the pipelines, environments, deployment patterns, and IaC that let agents and humans ship safely and repeatedly.Security and compliance by default. Secure engineering is an instinct, not a checklist. You understand the additional attack surface created when agents can access private data, consume untrusted content, and take actions in external systems. Permissions, isolation, secrets, sandboxing, auditability, and least privilege are part of the design from the start.AI quality. You instrument and evaluate AI behaviour so quality is measured, not assumed. You know that probabilistic systems need explicit evaluation, observability, and regression checks.Build for humans and agents. You design systems and interfaces that agents can operate as reliably as people can. Increasingly, the user is another agent, and the system needs to be legible, predictable, and observable to both.Build and grow the library. You create, maintain, and grow the team's shared library of agents, skills, integrations, prompts, evaluation patterns, and engineering workflows. You harvest every project for what belongs in it. This is core to the role, not housekeeping.The library is the asset that compounds: each reusable pattern makes the next problem easier to solve. Agents, skills, and patterns also decay without ownership, so keeping them useful is part of the work. How you work: the compounding loop The thing that separates this role from a traditional engineer who uses AI is that you build the machine, not just the output. The system you ship matters. So does what the team can now do that it couldn't do before. The patterns, skills, and tooling you sharpen are what make the impact compound. Start from golden paths. Compose against existing patterns and improve them. Reinventing something the team already knows how to solve creates drag for everyone who comes after you.Contribute back continuously. If you sharpen a pattern, improve a skill, or discover a better way to solve something, it gets contributed back to the shared system quickly rather than staying trapped inside one project.Protect machine-maintenance time. A meaningful slice of every cycle goes to hardening prompts, extending skills, documenting decisions, improving evaluations, and refining the harness. That investment is what makes the rest of the work compound.Commit to the AI-native way. AI is the default operating model here, not a side experiment running next to the old way. The toolset has a short half-life, so staying current with new model, agent, and harness capabilities is expected. So is the judgment to know when an agent, automation, or deterministic system is the right tool. If your instinct is to solve the problem in front of you and improve the system that solves the next one, you'll do great. What you bring Engineering- required Significant hands-on experience building and operating production software and systems.Real experience using AI coding harnesses such as Claude Code, Cursor, Windsurf, GitHub Copilot, or equivalents as part of your actual engineering workflow. You've shipped with them, not just demoed them.Strong experience building and operating APIs, integrations, and agent-facing systems. You are comfortable with MCP or have equivalent experience that makes it straightforward to pick up.A strong background in automation, integration, and AI in production.Secure coding practices and a security-first instinct, especially around sensitive data, system access, permissions, and external actions.DevOps and infrastructure-as-code experience across CI/CD, environments, deployment, and the tooling required to make those systems repeatable.A track record of building real systems that other people depend on, not just isolated features or prototypes.The ability to operate across a broad technical surface without needing a specialist at every boundary.Strong enough engineering fundamentals to know when agent-generated work is wrong, incomplete, unsafe, or unnecessarily complex. The specialty- your stalk You carry at least one area of genuine technical depth and stay sharp in it while operating broadly. AI expands the surface you can cover. It doesn't replace deep expertise. Your specialty might be: a specific cloud and its IaCdata engineeringsecurity and compliance engineeringplatform and reliabilityintegration architecturefull-stack product engineeringanother technically meaningful area where you have real independent authority We think of this as a mushroom-shaped technologist: a broad cap of capability, made wider through AI, anchored by genuine depth underneath. How you operate Ownership. You own a problem end to end, including dependencies and constraints outside your immediate remit.Comfort with ambiguity. You can take a vague brief, determine what actually needs to be solved, and produce a clear technical path forward.Bias to ship. You get something working in the real environment early and iterate from reality rather than polishing in isolation.Product sense. You care whether the system actually solves the problem. You understand the people and workflows around it, not just the implementation.Judgment over dogma. AI is the default source of leverage here, but you know when not to use it. You can tell the difference between automation that compounds and automation that adds complexity.Compounding instinct. You naturally turn repeated work into better tooling, reusable patterns, clearer interfaces, and systems the rest of the team can build on. This is not: A seat where you wait for fully specified tickets and execute them one at a time.A role where AI is a side tool you reach for occasionally.A place to rebuild from scratch what the shared library already solves.A role where prototyping quickly is enough. The systems you build need to survive contact with production.A traditional developer role. Your value isn't measured by how much code you personally write. You are a builder, and you need to be comfortable shipping work where agents produced most, or even all, of the implementation. You still own every decision about what gets built, how it works, and whether it is good enough to depend on. We’re looking for engineers who can go broad without becoming shallow, use AI without outsourcing judgment, and leave the system better than they found it. If that already sounds like how you work, you’ll probably feel at home here.