דרושים » הנדסה » junior AI Engineer & Implementer

Requirements:
10+ years of experience in software engineering, with at least 5 years in leadership roles managing diverse R&D teams
Proficiency in Software Development: strong experience in building and managing scalable systems using languages such as Go, Python, Java, or Node.js
Proven track record of delivering scalable cloud / SaaS services, operating in multi-cloud (AWS, Azure, GCP) and hybrid-cloud environments
Experience leading AI product (assistants, copilots, LLM-integrated workflows)
Exceptional leadership, communication, stakeholder, and customer management skills
Passion for empowering developers and enabling productivity through platform engineering
DevOps / DevSecOps experience - strong advantage
Growth mindset, with continuous learning in AI and agentic technologies.

Requirements:
4 years in Data Science, with industry experience in building and optimize AI/ML models
Specialized expertise in modern machine learning methods related to Natural Languages Processing (Transformers, Embeddings, LLMs)
Experience deploying AI/ML services and applications on at least one major cloud platform (Azure, AWS, GCP)
Pratrical skills using modern ML and Generative AI frameworks (Langchain, PyTorch, Tensorflow, Hugging Face and other open-source/ APIs and similar)
Experience with LLM applications and techniques, such as Retrieval-Augmented-Generation (RAG), Chane-of-Thought (CoT) prompting, and open-source LLMs fine tuning
Ability to write production ready code, with strong expertise in designing and implementing ML pipelines
Excellent problem-solving skills with a critical and creative mindset
Exceptional communication skills and a collaborative approach to teamwork
BSc, master or PhD in appropiate technology field (Computer Science, Electrical Engineering, Statistics, Applied Math, etc.)
A Big Plus If You Have:
Extensive experience in integrating and working with vector databases (e.g. Elastic, Milvus and more) for high-performance similarity search, semantic search and embeddings management.
Experience with building continuous integration and monitoring for Machine Learning applications.