Pelagon is a European defence technology company building Autonomous Maritime Interdiction systems. We bring together sensing, AI, software and maritime engineering to help protect critical infrastructure and maritime environments.
We are looking for a very senior, hands-on software engineer to own the software running in Pelagon’s operations centre: our data infrastructure, processing services, internal tools and customer-facing applications.
You will architect the overall platform and build it with the team, from data ingestion and storage to machine-learning workloads, operational applications and deployment. You will be the technical interface between our embedded systems and AI teams, bringing their work together into a coherent, reliable system.
This role requires depth across software development, data engineering and DevOps. Our software must run on infrastructure operated by Pelagon or our customers, including self-hosted and on-premises environments. Robustness, operability and cybersecurity must be designed into the system from the outset.
Software architecture and delivery. Define the architecture of our core services, APIs, internal tools and customer-facing applications. Make and document technical decisions, write production code and take responsibility for the system from design through operation.
Data infrastructure at scale. Build reliable ingestion, storage and retrieval for large volumes of sensor and operational data. Choose and operate databases and storage systems, design data models, and manage data quality, migrations, retention and performance.
Processing and machine-learning workflows. Build the infrastructure to schedule, execute and monitor data-processing and ML jobs. Handle dependencies, resource allocation, retries, reproducibility and failure recovery, working closely with the AI team to bring its workloads into production.
Deployment on infrastructure we control. Make the platform reproducibly deployable on Pelagon and customer infrastructure. Own infrastructure automation, software packaging, configuration, release pipelines, upgrades and rollback without depending on managed cloud services.
Reliability and operations. Establish observability, performance testing, capacity planning, backup and recovery. Diagnose production issues and build systems that recover predictably from interrupted connectivity, partial failures and faulty inputs.
Cybersecurity throughout the lifecycle. Build security into architecture, implementation, deployment and maintenance. Apply threat modelling, least-privilege access, authentication and authorisation, network isolation, secrets management, encryption and audit logging. Establish secure development practices, dependency and vulnerability management, patching and incident readiness.
Integration across teams. Define clear interfaces and data contracts with the embedded systems and AI teams. Own integration and end-to-end testing so devices, data pipelines, algorithms and applications operate as one system.
Engineering effectiveness. Set a high standard for code, testing, documentation and technical judgement. Mentor engineers and use AI-assisted development tools effectively to accelerate implementation, testing and investigation, while retaining rigorous review, security checks and control over sensitive code and data.
Extensive experience building and operating robust, data-intensive production software, with architectural ownership across multiple components and teams.
Strong hands-on software development skills, with the ability to move from system design to implementation and difficult production debugging.
Deep experience with data ingestion, databases, storage systems and distributed processing, including the practical challenges of performance, consistency and scale.
A strong DevOps and systems background: Linux, networking, containers, orchestration, infrastructure as code and automated delivery.
Proven experience deploying and maintaining software on self-managed infrastructure, private infrastructure or customer premises, including operating the services that managed cloud platforms would otherwise provide.
Experience building or operating job orchestration and data-processing platforms, and integrating ML workloads into production systems.
Practical cybersecurity expertise and a record of applying it to production software and infrastructure. You can identify threats, make security trade-offs explicit and implement effective controls.
The judgement to choose maintainable solutions, anticipate failure modes and balance delivery speed with long-term reliability.
The ability to lead technically across disciplines, clarify ambiguous requirements and take ownership of the overall outcome.
Effective use of modern AI engineering tools, with the discipline to verify generated code and protect confidential information.
Sensor, time-series, streaming or geospatial data.
Compute-intensive workloads, GPU infrastructure or ML platform engineering.
Enterprise software deployed across multiple customer environments.
Systems with demanding availability, security or connectivity constraints.
Operator-facing applications and software connected to physical systems.
You will have substantial ownership of the software foundation behind Pelagon’s operations, working directly with the founders, embedded systems engineers and AI team. Your architectural decisions and the code you ship will shape how the complete system is deployed, secured and operated.
We value demonstrated ownership: what you built, how it performed at scale, how you secured it and what you learned from operating it.