The Role

This is a hands-on engineering role at the frontier of AI-augmented software development. You will 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. You will 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 about 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.

• Implements evaluation datasets, prompt engineering, and observability.

• Contributes to design discussions and follows established guidelines.

Senior Engineer (typically 6–10+ years; 2+ leading AI deliveries)

• Leads end-to-end implementations and makes architecture choices.

• Designs data/retrieval schemas, instrumentation, and cost/latency budgets.

• Sets quality bars and mentors teammates.

Nice to Have:

Experience with agent orchestration frameworks, vector/graph databases, Postgres/Redis, tracing/observability platforms, Azure, or ERP integrations. Specific tools are flexible; equivalent experience is welcome.

Professional Qualities

• High ownership and follow-through. You don’t wait to be told what to do next. You see what needs doing and you do it.

• Pragmatic and quality-conscious. You use the simplest approach that solves the problem well. You don’t gold-plate, but you also don’t take shortcuts that create future pain, especially when the cost of doing it right is marginal.

• Reliability-first mindset. You treat guardrails, evaluation, and failure handling as core engineering work, not afterthoughts.

• Clear communicator. You write crisp messages, document decisions, and can explain technical trade-offs plainly.

• Dedicated and self-driven. You’re comfortable with autonomy and manage your own time effectively. You’re the kind of person who, when something is running, keeps an eye on it, not because someone asked, but because you care about the outcome.

What We Offer

• Competitive compensation and benefits.

• The opportunity to build production AI agents used by real businesses, measured by tangible KPIs.

• A modern, AI-native engineering environment where you’ll work with cutting-edge tooling daily.

• Autonomy to shape engineering standards and best practices.

• Career growth with executive visibility in a large, impact-oriented group.

Hiring Process

1. Initial interview: a structured conversation to understand your experience, thinking, and motivations.

2. Paid collaborative project: instead of a contrived take-home test, you’ll work on a real-scope assignment alongside other candidates over a ~2-week window, so you can fit it around your schedule. Top contributions will be awarded cash prizes. This is the core of our evaluation. We want to see how you work, not just how you talk about working.

3. Final interview: a conversation with the CEO of Aktor AI and the Head of Solution Delivery.

Our hiring process uses AI-assisted tools for application screening and includes an AI-powered interview stage. All decisions are made by people.