AI / Machine Learning Engineer Jobs in Greece | Information Technology / IT

60+ open positions from 10 companies

Search Jobs

About AI / Machine Learning Engineer Jobs

An AI / Machine Learning Engineer develops and implements intelligent algorithms and models to enable machines to learn and make decisions autonomously. They work with data scientists and software teams to design scalable AI solutions, automate processes, and improve business insights. This role is in high demand across industries like technology, finance, and manufacturing, offering significant career growth in AI innovation.

Find your next employer

Discover active opportunities with leading companies

Required Skills

Essential skills needed for AI / Machine Learning Engineer positions

PythonVery Common

Proficiency in Python programming language for software development, automation, and data analysis.

Prompt EngineeringCommon

The skill of designing and refining input prompts to optimize the output of AI language models.

Large Language ModelsCommon

Expertise in understanding and utilizing large-scale neural network models designed to process and generate human language.

Machine LearningCommon

Ability to develop algorithms and statistical models that enable computers to learn from and make predictions based on data.

SQLCommon

Proficiency in Structured Query Language for managing and querying relational databases.

Retrieval-Augmented GenerationCommon

A technique combining information retrieval and natural language generation to improve response relevance and accuracy.

Generative AICommon

Expertise in designing and implementing AI models that generate new content based on training data.

Java

Proficiency in Java programming language for developing cross-platform applications and backend systems.

Data Pipelines

Ability to design and manage data pipelines that efficiently process and transfer large volumes of data.

PyTorch

Experience with PyTorch, an open-source machine learning library for deep learning applications.

Additional Skills & Traits

Nice-to-have skills and soft skills that can help you stand out

MLOps

Practices and tools for deploying, monitoring, and managing machine learning models in production environments.

Docker

Experience with Docker containerization technology for developing, shipping, and running applications.

GPU Systems

Expertise in using Graphics Processing Unit systems for accelerating training processes in computing tasks.

Computer Vision

The field of computer science that enables computers to interpret and process visual data from the real world.

Kubernetes

An open-source platform for automating deployment, scaling, and management of containerized applications.

A/B Testing

A method of comparing two versions of a webpage or app to determine which one performs better.

Relational Databases

Knowledge of structured databases that store data in tables with relationships between them.

NoSQL Databases

Knowledge of non-relational database systems designed for distributed data storage and scalability.

Fine-tuning

The process of making small adjustments or improvements to optimize performance.

Communication

Ability to convey information clearly and effectively in verbal and written form.

Teamwork

Ability to collaborate effectively with others to achieve common goals.

Adaptability

Ability to adjust effectively to new conditions and changes in the work environment.

Problem Solving

Ability to identify issues and develop effective solutions efficiently.

Leadership

Ability to guide and motivate a group towards achieving goals effectively.

Curiosity

Desire to learn and explore new ideas, concepts, and experiences.

Interpersonal Skills

Ability to interact positively and effectively with others in a friendly and approachable manner.

Self-Motivation

Ability to drive oneself to take initiative and persistently pursue goals without external prompting.

Critical Thinking

Ability to analyze facts and form a judgment through thoughtful evaluation.

Continuous Learning

Commitment to constantly acquiring new knowledge and skills to improve oneself.

Educational Background

Recommended education and qualifications for AI / Machine Learning Engineer positions

Computer Science

Study of computational systems, programming, algorithms, and software development.

Engineering

Study of applying scientific principles to design, build, and maintain structures and machines.

Data Science

Study of methods to analyze, interpret, and extract knowledge from data using statistical and computational techniques.

Any Field

No specific academic field required; open to all educational backgrounds.

Mathematics

Study of numbers, quantities, shapes, and their relationships through abstract reasoning and logical deduction.

Artificial Intelligence

Study of algorithms and systems that enable machines to perform tasks requiring human intelligence.

Statistics

Study of data collection, analysis, interpretation, and presentation to make informed decisions.

Related Fields

Studies in disciplines related to the specified area of expertise.

Electrical and Computer Engineering

Study of electrical systems, electronics, computer hardware, and integrated technologies.

Software Engineering

Study of designing, developing, testing, and maintaining software systems.

Seniority Distribution

Entry15%
Experienced85%

Experience Requirements

Average experience: 2.5 years
0-2 years53%
3-5 years42%
6-10 years5%