Platform & Systems Engineer - Power Systems

**About enliteAI**

enliteAI builds AI and optimization systems for critical infrastructure, with a focus on electricity networks. We work with network operators and industrial partners on problems where the decisions carry real consequence: how a distribution grid is operated, how much capacity it can safely carry, and how flexible resources at the grid edge are coordinated. Our work runs from applied research through to systems that operate on real measurement data.

We are part of several EU Horizon research programmes — AI4REALNET, AI-EFFECT and INSIEME — and we maintain Maze, an open-source framework for simulation-based reinforcement learning. Our team combines reinforcement learning, optimization, data engineering and power systems expertise, and sits deliberately between academic research and industrial deployment. We are based in Vienna.

We are a small team and work like one: cross-disciplinary by default, low-ego, and more interested in whether something holds up than in who proposed it.

**The Role**

We are looking for a Platform & Systems Engineer to own how our systems are designed, deployed and run, and to take our technology from research-grade to production-grade.

Our research output has outgrown its engineering foundation. The methods work; the path from a working method to a reliably deployed service is slow and depends on too few people. Closing that gap is the job, and there is no inherited playbook for it — you would be defining what good looks like here rather than maintaining someone else's definition.

Three things sit at the centre of the role. **Architecture** comes first: our larger repositories need deliberate structure — module boundaries, interfaces, a testing strategy — so that more people can work in parallel without colliding. This is the highest-value part of the job, and the part most easily deprioritised, because it never arrives with a deadline attached. **Deployment** is second, including a repeatable path for standing up demos and proofs of concept, which is how our work reaches customers and project reviewers. **Infrastructure** is third: we run our own hardware in a Vienna datacenter, 11 nodes under a single Kubernetes cluster, and everything a managed platform would abstract away is ours — etcd, storage, the network, node lifecycle, GPU enablement.

You would join a team that combines reinforcement learning and optimization, data and platform engineering, and power systems expertise. You do not need to know how power grids work, that knowledge sits with colleagues.

## Tasks

• Shape the architecture of our larger repositories: module boundaries, interfaces, testing strategy, and the decisions that keep a growing codebase maintainable as more people work in it
• Build out and harden our deployment stack — containerised services on Kubernetes, packaged with Helm, with Redis and related components behind them
• Own the demo and proof-of-concept path, so that a presentable, isolated instance with seeded data can be stood up in hours rather than days
• Operate our Kubernetes cluster on our own hardware: node lifecycle, GPU enablement, storage, networking, capacity planning and upgrades
• Build the infrastructure beneath our data pipelines — MQTT and Kafka brokers, topic and retention design, orchestration, storage, and the backend services and APIs through which results reach applications and domain experts
• Build and harden CI/CD, developer tooling, and the internal platform our ML engineers and researchers work on daily
• Work alongside ML engineers, researchers and power systems experts on industrial and EU-funded projects

## Requirements

• Fluent English with strong communication skills — you can make an architectural case to people whose priority is research output, and come away with agreement rather than compliance
• Strong software engineering fundamentals: you think in systems, interfaces and failure modes, and can look at a growing codebase and say what will hurt in a year and why
• Solid Python, with experience in a substantial codebase that several people work on at once
• Kubernetes at the operator level — you have upgraded a cluster, replaced a node and debugged a networking or storage problem, not only deployed workloads into a platform someone else runs. Helm charts authored, not just installed
• Self-directed, you don't need a detailed roadmap to make progress
• Low-ego and collaborative — much of this role means restructuring code that other people wrote and care about
• A degree in computer science or a related field, or equivalent practical experience
• Valid work permit for Austria

**It would be great if you**

None of this is required, and nobody has all of it — any one is a useful signal.

• Have run stateful infrastructure in production — Kafka and MQTT brokers especially, but also Redis or databases — with a real feel for persistence, delivery semantics and failure behaviour
• Have built out infrastructure-as-code and observability rather than inheriting someone else's
• Have worked with pipeline orchestration, time series at scale, GPU scheduling on Kubernetes, or Ray
• Have built lightweight PoC frontends, or know your way around power grids
• Speak German, which helps when working directly with Austrian and German network operators

## Benefits

• Real ownership: this role exists to change how we build and ship, with a mandate to make architectural decisions rather than implement someone else's
• Infrastructure you actually own — our own Kubernetes cluster on our own hardware, rather than a managed platform with the interesting parts abstracted away
• A team spanning reinforcement learning, optimization, data and platform engineering, and power systems, with the domain expertise to tell you why a constraint exists
• Work at the interface of academia and industry: our EU Horizon projects mean the systems you build are used by research consortia as well as by customers
• Work on critical infrastructure that matters: the energy transition is, at heart, a systems problem
• Hybrid working: 2–3 days per week at our office in Vienna's 1st district, with minimal core hours
• Dedicated time and budget for R&D, conferences and professional development

**Job Types:** Full-time or Part-time (min. 30h)

**Salary Range:** > €65,000 annually (based on full-time), depending on experience and qualifications.

Tags: Platform Engineering, DevOps, Kubernetes, Helm, Kafka, MQTT, Redis, Python, Software Architecture, Smart Grids, Energy Systems