(Senior) AI Solutions Engineer - Remote (Europe/Asia)

Aktor Group of Companiesis a leading player in Greece's infrastructure sector, delivering high-end services across Construction, Real Estate, Renewables, Facility Management, and Equity Participations.

Aktor Group of Companies is a leading player in Greece's infrastructure sector, delivering high-end services across Construction, Real Estate, Renewables, Facility Management, and Equity Participations. With 6,100 employees, we build the infrastructure that empowers people and communities to thrive.

Aktor AI () is our technology arm, building the next generation of enterprise AI. We deploy fully-managed AI employees that self-train on organisational processes and reach 95%+ accuracy within days.

Our agents handle finance operations, contract management, support workflows, and more, with built-in supervision that audits decisions, escalates uncertainty, and learns continuously. We don't sell software licences. We deliver outcomes.

We are hiring a (Senior) AI Solutions Engineer to join our AI engineering team remotely and help us build, harden, and scale these agents.

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.

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