דרושים » הנדסה » AI Applied Researcher

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:
5+ years of experience in AI research, with a strong background in LLM research
Advanced proficiency in Python and hands-on experience with leading frameworks and libraries, such as PyTorch, Hugging Face, torchtune, and vLLM
M.Sc. or Ph.D. in Computer Science, Data Science, Statistics, Engineering, Mathematics, Physics, or a related field. Preferably with applied focus on machine learning, computer vision, NLP, or deep learning
Fast learner with excellent problem-solving skills
Positive attitude, intellectual curiosity, and eagerness to learn and share knowledge
Passion for medicine, health, and wellbeing, with a drive to make a meaningful impact
Proven experience translating deep learning research into reliable, high-impact production systems
Collaborative team member who contributes to shared technical decisions and knowledge sharing
(Preferred) Experience with medical data or medical AI

Requirements:
Master's degree in Computer Science, Mathematics, Statistics, Data Science, or related field; a PhD is an advantage.
Has 3+ years of experience with statistical and machine learning tools - Python, R, etc.
Strong Python programming skills
Strong understanding of ML/NLP fundamentals (transformers, embeddings, language models, fine-tuning concepts)
Experience building LLM-based systems beyond basic API calls (e.g., multi-step pipelines, structured outputs, error handling, fallbacks, agentic flows)
Strong data analysis skills and familiarity with evaluation methodologies & metrics
High proficiency with AI-assisted development tools (Cursor, Claude Code, Copilot, or similar)
Solid experience with APIs, databases, and software engineering best practices
Excellent problem-solving abilities and attention to detail
Strong communication skills in Hebrew & English

Requirements:
Advanced knowledge and experience in NLP and use of LLMs for GenAI applications in production at scale.
Strong experience in designing end-to-end R&D plans for GenAI including evaluation and validation lifecycle and benchmarking.
Strong proficiency in Python
Solid understanding of Data Science and Machine Learning lifecycle and best practices evaluating and validating AI systems at scale.
Excellent problem-solving abilities, coupled with a creative and strategic mindset.
Proven ability to work effectively in a team setting.
Advantages:
Experience with EDD (evaluation driven development) for GenAI applications.
Familiarity with cybersecurity applications of GenAI.
Advanced skills in performance optimization for high throughput systems.
Tech Stack:
Python, Langchain, Langgraph (or other agentic frameworks), Langfuse/LangSmith (or other observability and tracing tools), HuggingFace, Mlflow, MongoDB

Requirements:
MSc in Computer Science/Engineering or related field with a focus on applied statistics, AI, machine learning, or related fields - a must.
6+ years of production environment experience, working with predictive and probabilistic models, clustering algorithms, classification models, search and recommendation techniques - a must.
Proven record for successful production implementation of translating product/business requirements into a technical solution.
Experience with Large Language Models (LLMs), including fine-tuning, prompt/context engineering, and working with modern AI/LLM toolchains.
Team player, Ability to handle several tasks simultaneously.
Strong knowledge in Python, SQL, and NoSQL.
Spark and Big data technologies experience - an advantage.
Research experience - an advantage.
Experience with e-commerce - an advantage.

Requirements:
Advanced knowledge and experience in Computer Vision and Natural Language Processing, engineering aspects of developing ML and GenerativeAI models at scale.
Experience designing and executing end-to-end research and development plans and generating impact through large-scale machine learning model development. Preferably evidenced by peer-reviewed publication, patents, open sourced code or the like.
Relevant work or academic experience (MSc + 5 years of working experience, or PhD + 3 years of working experience), involved in the application of Machine Learning to business problems.
Masters degree, PhD or equivalent experience in a quantitative field (e.g. Computer Science, Engineering Mathematics, Artificial Intelligence, Physics, etc.).
Experience on multiple machine learning facets: working with large data sets, model development, statistics, experimentation, data visualization, optimization, software development.
Experience collaborating cross functionally in the development of machine learning products (e.g. Developers, UX specialists, Product Managers, etc.).
Strong working knowledge of Python, Java, Kafka, Hadoop, SQL, and Spark or similar technologies. Working experience with version control systems.
Excellent English communication skills, both written and verbal.
Successfully driving technical, business and people related initiatives that improve productivity, performance and quality while communicating with stakeholders at all levels
Leading by example, gaining respect through actions, not your title. Developing your team and motivating them to achieve their goals. Providing feedback timely and managing your key team performance indicators.

Requirements:
B.Sc. in Computer Science, Engineering, Data Science, or a related quantitative field.
3+ years of experience in AI/ML engineering, solutions engineering/architecture, or related technical roles.
Hands-on experience with modern AI/ML workflows, including large language models (LLMs), MLOps, or production-grade model deployment.
Proven track record of engaging with enterprise customers, including pre-sales activities (demos, PoCs, workshops).
Strong software engineering and/or data engineering fundamentals (e.g., APIs, cloud services, data pipelines).
Ability to work independently while managing multiple client-facing projects.
Excellent communication skills, with the ability to explain complex AI concepts to both technical and business stakeholders.
Customer-first mindset, with a focus on delivering practical, value-driven AI solutions.

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:
What youll need:
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:
Bachelors degree in computer science, Engineering, or a related field, or equivalent practical experience.
At least 5 years of professional experience as a software engineer building and operating production systems.
Experience in designing and deploying end-to-end GenAI solutions.
Solid understanding of software engineering fundamentals, including data structures, algorithms, testing, and version control.
Ability to collaborate effectively across teams and communicate complex technical concepts clearly.
Experience building cloud-based services on platforms such as Azure, AWS, or GCP.
Preferred
Master's Degree in Computer Science or related technical field AND 6+ years technical engineering experience with coding in languages including, but not limited to, C, C++, C#, Java, JavaScript, or Python
Hands-on experience designing and building solutions using AI/LLM frameworks and tooling (e.g., Semantic Kernel, LangChain, vector databases).
Knowledge of secure software development practices and threat modeling.
Experience applying responsible AI principles, including safety, evaluation, and governance.
Knowledge of the cybersecurity domain is a plus, including familiarity with security threats, attack vectors, detection and response concepts, and modern security solutions.