דרושים » הנדסה » Senior AI Engineer

Requirements:
6+ years of experience in software engineering, machine learning, data science, or related technical roles
3+ years of hands-on experience building machine learning or AI systems in production
Strong experience working with textual data and NLP techniques such as embeddings, classification, semantic search, or information extraction
Hands-on experience building applications powered by LLMs (e.g., prompt pipelines, RAG systems, agents, or structured extraction)
Comfortable leveraging AI-powered developer tools (e.g., Cursor, Claude Code, Copilot, ChatGPT) to accelerate development and experimentation
Strong product intuition - you focus on solving real user problems, not just building models
Excellent collaboration and communication skills
Degree in Computer Science, Engineering, or a related technical field - or equivalent practical experience

Requirements:
What we need to see:

MS/PhD with expertise in Computer Science, Computer Engineering, Electrical Engineering or related field with a focus on Deep Learning or Machine Learning.

5+ years of experience in deep learning and machine learning in a production environment.

Excellent Python programming skills, strong software design fundamentals, and experience leveraging coding agents in development workflows.

Hands-on experience with deep learning development frameworks and libraries (e.g. TensorFlow, PyTorch).

Experience with large scale production systems and pipelines, with a track record of developing production-grade models

Experience with agentic AI systems, agent frameworks, and evaluation of agent performance and reliability.

Strong algorithm development experience, with knowledge of inference optimization techniques such as model distillation, quantization, pruning.

Background with algorithms including zero/few-shot learning, self-supervised and unsupervised learning and generative AI models for synthetic data creation.

Experience with fine-tune / training LLM models

You are proactive, take full ownership of your deliverables, have a can-do approach, and are excited to learn, explore and apply your skills and creativity to some of the most challenging and rewarding problems in the field.


What will make you stand out from the crowd:

Strong software development experience.

Familiarity with GPU based technologies like CUDA, CuDNN and TensorRT.

Experience with tools for data processing and storage.

Security and networking background, with knowledge of security protocols, network architectures, firewalls, intrusion detection systems, and other relevant security and networking concepts.

Requirements:
5+ years of experience in Data Science / Applied ML, with strong hands-on experience building and deploying NLP and LLM-based solutions in production environments.
Experience in Data Security or Cyber Security is a strong advantage, particularly in areas such as document classification, semantic search, or enterprise data analysis.
Strong Python skills and experience with modern ML/DL frameworks such as PyTorch or TensorFlow, including transformer architectures and fine-tuning workflows.
Experience working with large-scale datasets and production ML systems, including tools such as SQL, Spark, or similar distributed data processing frameworks.
Experience with MLOps, scalable model deployment, inference optimization, CI/CD practices, and the end-to-end ML lifecycle from data preparation to production monitoring.
Experience working closely with software engineering teams in cloud-native environments; familiarity with AWS/Azure/GCP, Kubernetes, and microservices architectures is a plus.
Track record of delivering impactful AI solutions in real-world production settings, with strong communication, collaboration, and independent problem-solving skills.
Ph.D. or M.Sc. in Computer Science, Engineering, Mathematics, or a related quantitative field is a plus, but not required with equivalent industry experience.

Requirements:
BSc or MSc in Computer Science, Data Science, or related field (research experience is an advantage).
3+ years of experience in one or more of the following areas: deep learning, machine learning, NLP, or computer vision.
Proficiency in Python and software architecture design.
Ability to write clean, testable, production-grade code.
Strong problem-solving skills and the ability to learn quickly.
Team player with excellent communication skills in English, both verbal and written.

Requirements:
Technical Expertise:
Programming: Advanced proficiency in Python for backend development and AI applications
GenAI Mastery: Deep understanding of large language models (LLMs) and experience with major model APIs (OpenAI, Anthropic, Google, etc.)
Multi-Agent Systems: Expertise in designing and implementing GenAI multi-agent architectures
Prompt Engineering: Advanced skills in prompt design, optimization, and engineering techniques
Cloud Platforms:
Required: Hands-on experience with AI services in at least one major cloud platform (GCP, Azure, or AWS)
Advantage: Experience across multiple cloud platforms (AI Search, Vertex AI, SageMaker, etc.)
Development Frameworks: Experience with GenAI frameworks like LangChain and cloud-based retrieval services
Software Engineering: Strong background in microservices architecture, API development, and production system design
AI/ML Fundamentals: Solid understanding of deep learning principles and GenAI techniques
Containerization (Advantage): Experience with Docker and Kubernetes for deployment and orchestration
OCR Technologies (Advantage): Experience with Optical Character Recognition systems and document processing
Data Pipelines (Advantage): Experience building and maintaining data processing pipelines
Professional Experience
Mid+ Level Experience: 2+ years in AI/ML development with significant GenAI project experience
Production Systems: Proven track record of deploying and maintaining AI solutions in production environments
Client-Facing Experience: Comfortable with technical presentations and requirement gathering sessions
Education & Background
Preferred: Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or related technical field
Alternative: Demonstrated industrial experience in developing deep learning and GenAI solutions (degree not required with strong portfolio)
Soft Skills
Problem-Solving: Excellent analytical and creative problem-solving abilities
Communication: Strong technical communication skills for both technical and non-technical audiences
Collaboration: Proven ability to work effectively in cross-functional teams
Adaptability: Thrives in fast-paced environments and eager to learn emerging technologies
Consulting Mindset: Ability to understand client needs and provide strategic technical guidance.

Requirements:
Must-have:
5+ years of experience in backend or AI engineering with strong coding skills (Python preferred).
Proven experience building and deploying production-grade AI/ML systems.
Strong software engineering fundamentals (data structures, algorithms, system design).
Experience with distributed systems, microservices, and cloud platforms (AWS/GCP/Azure).
Hands-on experience with LLMs and generative AI, including prompt engineering and model integration.
Experience with LLM frameworks and agent orchestration tools (e.g., LangChain, CrewAI, ADK, or similar).
Strong debugging and problem-solving skills, with an ownership mindset.
Nice-to-have:
Experience with ML frameworks such as PyTorch or TensorFlow.
Experience with MLOps tools and practices (MLflow, Kubeflow, CI/CD for ML).
Background in NLP, LLM optimization, or agent-based systems in production.
Experience with large-scale data pipelines and NoSQL databases.
Experience with model evaluation, monitoring, and continuous improvement in production environments.
Contributions to open-source projects or research publications.

Requirements:
Excellent coding skills in Python/TypeScript, with at least 5 years of hands-on experience building reliable backend services, agents and tooling. Familiarity with modern ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face) is a strong advantage.
Experience designing, deploying, and maintaining production systems that integrate ML components, including APIs, microservices, model serving layers, feature pipelines, monitoring, and CI/CD/MLOps workflows.
Solid experience with AI related contexts Understanding of prompt engineering and LLM optimization techniques, RAG architecture
Solid understanding of distributed systems concepts, performance optimization, observability, and operating services at scale.
Strong communication skills, with the ability to bridge technical, product, and business perspectives.
Prior experience in cybersecurity, fraud prevention, or identity management is a plus, especially with secure system architectures or ML-augmented decisioning systems.
Advantages
Experience integrating with LLM APIs (OpenAI, Anthropic Claude, etc.)
Experience with agent frameworks (LangChain, LlamaIndex, AutoGPT)
Background in ML/AI concepts and model deployment
Experience with message queues (RabbitMQ, Kafka) and event-driven architectures
Experience with function calling and tool use patterns in LLMs

Requirements:
BSc or higher in Computer Science, Mathematics, Statistics, or related fields.
4+ years of hands-on experience as a Data Scientist, ideally within mobile, gaming, or social network industries.
Proven experience with AI/ML frameworks and toolkits (Scikit-learn, TensorFlow, PyTorch, LangChain, etc.)
Familiarity with MLOps best practices, model versioning, experiment tracking, and continuous deployment.
Strong knowledge of machine learning techniques: Classification, regression, segmentation, reranking, model interpretability
Solid background in data analysis and statistics; ability to design experiments and interpret results.
Experience working in cloud environments, especially Google Cloud Platform (GCP) - BigQuery, GCS, Vertex AI (a plus).
Comfortable working in fast-paced, production-critical environments with a sense of ownership and accountability.
Advantages:
Experience developing recommendation engines, especially with deep learning and reranking techniques.
Experience with LLM-based applications, prompt engineering, Agents development, etc.

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.

Requirements:
Who You Are:
Youre a backend engineer with at least 5 years of professional experience who values code simplicity and performance. Youve shipped software that uses LLMs to production
You are passionate about Generative AI and its ability to help software engineers be more productive
You have relevant experience with Large Language Models, large-scale systems and data sets, machine learning, or adjacent fields
You are a great community ambassador and can drive hard technical conversations towards a pragmatic solution
You want to work in a fast, high-growth startup environment that respects its engineers and customers
You have experience writing classic or AI oriented developer tools(such as IDEs, static code analysis, SDLC tools etc).
You have a BS/MS/PhD in a Computer Science, Engineering or related scientific field or equivalent experience.