AI Systems Engineer

DEUS EX MACHINA

. Design and build AI systems end to end across backend, data engineering, AI systems, and architecture.

Posted 9/16/2026full-timeThessaloniki • GreeceJuniorMid-Level💰 €1,500 - €3,500 per monthWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in designing and building AI systems, including backend services, data engineering, and architecture. Proficient in implementing production AI systems and managing complex data workflows while ensuring reliability, security, and maintainability.

Highest-signal resume keywords

Python ProgrammingAI Systems DevelopmentAPI Design and DevelopmentData Pipeline OrchestrationCloud Services (AWS, Azure, GCP)

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Applicant Tracking System Keywords

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Hard Skills

Software Engineering FundamentalsSQLETL/ELTData ModelingLLM APIsMicroservicesData QualityFastAPIDjangoFlask

Soft Skills

Results-OrientedClear Communication

Tools & Technologies

DockerKubernetesGitHub CopilotClaude CodeCursor

Industry Keywords

Knowledge GraphsSemantic SearchBioinformaticsAgentic WorkflowsData-Intensive Applications

Tech Stack

Tools & technologies

AWSAzureCloudDjangoDockerETLFlaskGoogle Cloud PlatformKubernetesMicroservicesNoSQLPostgresPythonSQL

About the role

Key responsibilities & impact

Design and build AI systems end to end across backend, data engineering, AI systems, and architecture.
Build agentic AI systems using LLMs, retrieval, tools, memory, orchestration, and human-in-the-loop patterns.
Decide when agents are appropriate and when deterministic software is better.
Implement production AI systems across backend services, data infrastructure, agentic workflows, and cloud components.
Design and build APIs, services, event-driven workflows, and distributed components connecting applications, models, and data.
Build and operate pipelines for structured, unstructured, scientific, and external data, including ingestion, transformation, indexing, lineage, and quality controls.
Design retrieval and knowledge architectures using relational databases, vector stores, search systems, knowledge graphs, and other data stores.
Turn prototypes into production systems by defining interfaces, failure modes, observability, testing, security, performance, and deployment strategy.
Make and document architectural decisions involving cost, reliability, maintainability, scalability, and model performance tradeoffs.
Work directly with data scientists, bioinformaticians, product managers, and engineers while maintaining technical integrity of the complete system.
Deliver major systems from feature ideation through production operation with few handoffs between backend, data, and AI teams.
Bring new AI capabilities to production quickly without sacrificing reliability, testability, security, or maintainability.

Requirements

What you’ll need

Solid software engineering fundamentals and at least 2 years of hands-on experience building production systems in Python, or a comparable language.
An appetite for simplicity and results-oriented approach to delivery.
Actual experience working with backend systems, APIs, microservices, and data-intensive applications.
Practical experience building with modern AI systems, including LLM APIs, RAG, embeddings, tool use, agentic workflows, evaluation, and production integration.
Experience utilizing AI-assisted development tools such as Claude Code, GitHub Copilot, or Cursor.
Hands-on experience with SQL, data modeling, ETL/ELT, pipeline orchestration, data quality, and large or heterogeneous datasets.
Strong English and ability to explain technical decisions clearly to engineering, product, and scientific teams.
Experience with Python frameworks such as FastAPI, Django, or Flask; PostgreSQL and one or more NoSQL, vector, graph, or search technologies is a strong plus.
Experience with AWS, Azure, or GCP; Docker and containerized deployments; Kubernetes or other orchestration platforms is a strong plus.
Experience with knowledge graphs, semantic search, ontology-driven systems, scientific data platforms, or bioinformatics applications is a strong plus.
Experience evaluating and operating LLM/agent systems in production, including tracing, guardrails, model routing, prompt/version management, and cost/latency optimization is a strong plus.
Solid understanding of system design, including queues, caching, concurrency, storage choices, service boundaries, fault tolerance, observability, and cloud deployment is a strong plus.
Latest CV in English.

Benefits

Comp & perks

Flexible work schedule.
Professional and personal development opportunities.
Private life & health insurance.
Wellbeing activities.
Room to experiment, learn, and have fun.
Peers with big smiles and fascinating ideas.
A multidisciplinary, multinational team that values trust, autonomy, and innovation.
Resources and advanced tools necessary to deliver best-in-class results.
Workplace that inspires collaboration and creativity.
Environment that promotes professional growth and work-life balance.