ML Engineer

EFA GROUP
comprises companies in Aerospace, Security, Defense, and Industrial Cooperation with a solid international presence. EFA GROUP currently employs more than
400 people
, the majority of whom are engineers and scientists. Headquartered in Greece, the Group maintains a strong international presence across
Europe
(Cyprus, Belgium, Switzerland, Estonia, and Germany),
Asia
(Singapore, Indonesia, and South Korea),
the Middle East
(UAE), and
the
United States
, serving customers in 34 countries worldwide.

The GROUP includes
EFA VENTURES
(Supply Chain Management and integrated services),
AEROSPACE VENTURES
(Industrial Participation and related services),
SCYTALYS
(Systems Integration and Software),
ES SYSTEMS
(MEMs & IoT Integration),
EPICOS
(Global Defense B2B Information Platform),
UCANDRONE
(Unmanned Systems),
AETHER AERONAUTICS
(Target Drones),
STHENOS AI
(Intelligence Solutions),
THYREOS CYBER
(cyber security)
, SUPERIOR AIR
(aviation services and specialized aerial missions),
SSMART
(defense hardware - software production and services).

STHENOS AI
is the AI developer of
EFA Group
, building intelligent, mission-ready solutions for defense and aerospace. With deep expertise in Command-and-Control (C2), cyber defense, computer vision, and autonomous systems, we design and deploy secure, field-proven AI that enhances operational efficiency and situational awareness. As part of a leading European defense ecosystem, we bring scalable innovation where it matters most - in the theater of operations. Internally, we are building a unified operations platform: a single system through which the entire company will run its daily work, with AI woven through every workflow. We have validated the concept with a working prototype and are now assembling a small senior team to build the production platform properly, from the ground up.

The Role

STHENOS AI
is looking for an
ML Engineer
to support the development and deployment of next-generation AI solutions. In this role, you will work closely with Data Scientists and Software Engineers to transform machine learning models into reliable, scalable, and production-ready products. You will contribute across the entire AI lifecycle, from data processing and model deployment to MLOps, monitoring, and continuous improvement.

Key Responsibilities

• Productionize machine learning models and AI solutions, ensuring reliability, scalability, and maintainability
• Support the monitoring, troubleshooting, and continuous improvement of deployed models
• Ensure data quality, integrity, and security across AI products and data pipelines
• Design and implement data processing workflows and reusable data engineering components in collaboration with Data Scientists
• Contribute to the evolution of the company's analytics platform by evaluating and implementing new tools and services
• Take ownership of MLOps activities, including model deployment, monitoring, versioning, and lifecycle management
• Build and maintain CI/CD pipelines to enable efficient and compliant delivery of data and AI products
• Collaborate with cross-functional teams to translate business and technical requirements into robust solutions
• Participate in internal and external technical communities, meetups, and conferences, sharing knowledge and best practices

Requirements

• Experience developing data processing workloads using modern frameworks such as Apache Spark or Microsoft Fabric
• Bachelor's or Master's degree in Computer Science, Statistics, Informatics, Data Science, or a related quantitative field
• Strong proficiency in Python and data-related libraries such as Pandas and NumPy
• Experience working with data storage formats such as Parquet, Avro, ORC, JSON, and CSV, with a solid understanding of their use cases and trade-offs
• Experience with large-scale data processing and distributed computing environments
• Strong interest in the end-to-end development, deployment, and operation of AI products
• Understanding of MLOps concepts and best practices
• Experience designing and implementing APIs is considered an advantage
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud Platform is considered an advantage
• Experience with Spark, Ray, or similar distributed computing frameworks through professional or academic projects

Nice to Have

• Experience with containerization technologies such as Docker and Kubernetes
• Exposure to CI/CD tools and DevOps practices
• Familiarity with model monitoring, observability, and performance optimization
• Knowledge of version control systems, particularly Git