(Senior) AI Solutions Engineer
Overview
are hiring a (Senior) AI Solutions Engineer to join our AI engineering team remotely and help us build, harden, and scale AI agents. .
engineering role at the frontier of AI-augmented software development.
which specific frameworks you’ve used and far more about whether you make good decisions. Technologies change fast; judgment compounds.• Strong software engineering fundamentals. You understand how systems are built, how they break, and how to keep them simple. • Experience with LLM-based systems in production or near-production settings (agents, RAG, automation, orchestration). • A product-minded approach. You think about what you’re building and why, not just how.You understand that the goal is a working solution that serves a business need. • Architectural taste. You can look at a system design, whether you wrote it or an AI generated it, and tell whether it’s sound, over-engineered, or cutting corners that will cost you later. • Comfort with ambiguity. AI systems don’t behave like deterministic code.You’re comfortable with probabilistic outputs, evaluation-driven development, and iterative refinement. Levels Engineer (typically 2–5 years total; 1+ with AI systems) • Ships features and integrations independently. •
Requirements
design, build, and ship production AI agents, but the way you build them may look different from what you’re used to. Our engineers work heavily with AI coding assistants and autonomous agent tooling.The craft has shifted: writing every line yourself matters less than knowing what good looks like. Making sound architectural decisions, maintaining quality standards, and steering AI-generated output toward well-structured, maintainable systems.
spend more time thinking, reviewing, and deciding than typing.That said, you still need strong engineering fundamentals. You can’t evaluate what you don’t understand. The best engineers in this new paradigm are the ones who know when the AI is wrong and can articulate why.What You’ll Do • Build and ship AI agents end-to-end: from scoping and prototyping through optimisation and production rollout. • Architect for reliability: design observability, logging, guardrails, evaluation pipelines, cost controls, and human-in-the-loop flows. • Integrate with enterprise systems: build connectors and retrieval pipelines to data stores, APIs, and line-of-business applications.• Review and elevate: review AI-generated code and designs with a critical eye. Challenge what doesn’t meet the bar, accept what does. • Own quality: write and maintain evaluation datasets, define accuracy targets, and treat failure modes as first-class engineering concerns.• Document clearly: produce designs, runbooks, and technical documentation that others can follow. What We’re Looking For Engineering Judgment Over Stack Knowledge We care far less