machine learning engineer
12 Αυγ 2026 · SHOPFLIX.gr
Τι θα κάνεις
- Σχεδιάζεις, κατασκευάζεις και αναπτύσσεις συστήματα AI για βελτίωση ανακάλυψης προϊόντων και εμπειρίας χρήστη
- Δημιουργείς λειτουργίες οπτικής αναζήτησης, ομοιότητας εικόνων και ταυτοποίησης προϊόντων
- Αναπτύσσεις συστήματα σημασιολογικής αναζήτησης και πυκνής ανάκτησης
- Σχεδιάζεις και αναπτύσσεις εμπειρίες με χρήση LLM για ευφυείς αναζητήσεις και διεπαφές συνομιλίας
- Κατασκευάζεις αυτόνομες ροές εργασίας με agentic AI frameworks
- Κλιμακώνεις ασύγχρονα φορτία εργασίας ML και αγωγούς δεδομένων με προσανατολισμό στα συμβάντα
- Αναπτύσσεις, παρακολουθείς και βελτιστοποιείς APIs ML σε περιβάλλοντα cloud
Βασικές προϋποθέσεις
- Γνώσεις Python, PyTorch, TensorFlow και Hugging Face
- Εμπειρία σε Computer Vision, Image Embeddings και Feature Extraction
- Γνώσεις σε Natural Language Processing, Large Language Models και Prompt Engineering
- Εμπειρία με Semantic Search, Vector Databases (Pinecone, Milvus, Qdrant) και Dense Retrieval
- Γνώση Frameworks agentic AI όπως LangChain, LlamaIndex, CrewAI
- Εμπειρία σε MLOps, Cloud Platforms (AWS, GCP), Docker και Kubernetes
- Γνώσεις σε Distributed Task Queues και Message Brokers όπως RabbitMQ
- Γνώσεις SQL
Παροχές
- Υβριδικό μοντέλο εργασίας, ανταγωνιστικό πακέτο αποδοχών, ευκαιρία καθημερινού αντίκτυπου στην ελληνική αγορά e-commerce, σύγχρονο agile περιβάλλον, πρόσβαση σε προγράμματα εκπαίδευσης και εργαλεία ανάπτυξης δεξιοτήτων.
Περιγραφή Θέσης
We are looking for a Machine Learning Engineer to join our engineering team and help us build intelligent, scalable, production-grade AI systems.
This is a hands-on engineering role for someone with a strong multi-disciplinary toolkit across computer vision, natural language processing, information retrieval, generative AI, and production MLOps.
🧩 What You Will Do
You will help design, build, and deploy AI-powered systems that improve product discovery, search relevance, recommendation quality, image understanding, and conversational user experiences across our marketplace.
Your work may include:
Building image similarity, product matching, and visual search capabilities.
Developing semantic search and dense retrieval systems.
Designing and deploying LLM-powered experiences for intelligent search and conversational interfaces.
Creating autonomous workflows and tool-use systems using agentic AI frameworks.
Scaling asynchronous ML workloads and event-driven data pipelines.
Deploying, monitoring, and optimizing production ML APIs in cloud environments.
✅ What We Are Looking For
Strong software engineering fundamentals and ability to ship reliable production systems.
A pragmatic engineer who can turn AI concepts into production-ready systems.
Strong problem-solving ability and ownership mindset.
Interest in e-commerce, marketplace platforms, search, discovery, and user experience.
Ability to collaborate with engineering, product, and business teams.
Curiosity, adaptability, and enthusiasm for building with emerging AI technologies.
As well as, experience in some or most of the following areas:
Computer Vision & Image Similarity
Feature extraction, image embeddings, and high-dimensional similarity matching.
Deep metric learning and visual search systems.
OCR and text extraction from images.
Advanced Information Retrieval
Dense retrieval, vector search, and semantic search optimization.
Experience with vector databases such as Pinecone, Milvus, or Qdrant.
Practical understanding of ranking, relevance, and retrieval evaluation.
Generative AI & Large Language Models
Prompt engineering, fine-tuning, and deploying LLMs.
Experience building LLM-powered search, recommendation, or conversational interfaces.
Familiarity with Hugging Face models and modern NLP tooling.
Agentic AI Frameworks
Building autonomous workflows and tool-use systems.
Experience with LLM orchestration frameworks such as LangChain, LlamaIndex, or CrewAI.
Designing reliable multi-step AI pipelines.
Asynchronous Systems & Queuing
Experience with distributed task queues and message brokers.
Practical knowledge of systems such as RabbitMQ.
Building event-driven data pipelines and asynchronous ML workloads.
Production MLOps
Deploying, monitoring, and scaling ML services in production.
Experience with AWS or GCP.
Working with Docker, Kubernetes, and low-latency ML APIs.
Understanding of model observability, performance monitoring, and reliability.
Core Engineering
Strong programming skills in Python.
Solid knowledge of SQL.
Experience with ML frameworks such as PyTorch, TensorFlow, and Hugging Face.
🎁 What We Offer
Hybrid working model
Competitive compensation package
The opportunity to work in a high-traffic environment (millions of users) where your code impacts the Greek e-commerce market daily.
A modern, agile work environment (Tribes & Chapters).
Access to training programs and skill-development tools




