לוח משרות דרושים AI Engineer

Requirements:
Requirements: - Bachelor’s degree in Computer Science, Software Engineering, Mathematics, Physics, or a related field - At least 3 years of hands-on experience in algorithm development using C++ - Must - Proficiency in SQL- Must - In-depth knowledge of algorithms, physics, and mathematics - Must - Strong analytical, and problem-solving skills - Familiarity with radar systems - Advantage - Experience as a Tech Lead - Advantage

Requirements:
B.Sc. in Computer Science, Electrical Engineering, or Mathematics
Experience in GenAI tools such as LLM Orchestrations and integration packages, Agents, RAG systems
5+ years expertise with modern backend programming languages such as Python/Java/Scala/Kotlin/Go
Experience writing code in Agile, CI/CD-based production environments
Experience working with Big data technologies such as Kafka/Spark/OpenSearch, Parquet/Avro/Delta Lake/Iceberg
Strong communication and collaboration skills
Data-engineering, architecture, data-science roles or security domain knowledge are an advantage

Requirements:
At least 10 years of experience in a data engineering role, including 2+ years as a data & AI architect with ownership over company-wide architecture decisions.
Proven experience designing and implementing large-scale, Big Data infrastructure from scratch in a cloud-native environment (GCP preferred).
Excellent proficiency in data modeling, including conceptual, logical, and physical modeling for both analytical and real-time use cases.
Strong hands-on experience with:
Data lake and/or warehouse technologies, with Apache Iceberg experience required (e.g., Iceberg, Delta Lake, BigQuery, ClickHouse)
ETL/ELT frameworks and orchestrators (e.g., Airflow, dbt, Dagster)
Real-time streaming technologies (e.g., Kafka, Pub/Sub)
Data observability and quality monitoring solutions
Excellent proficiency in SQL, and in either Python or JavaScript.
Experience designing efficient data extraction and ingestion processes from multiple sources and handling large-scale, high-volume datasets.
Demonstrated ability to build and maintain infrastructure optimized for performance, uptime, and cost, with awareness of AI/ML infrastructure requirements.
Experience working with ML pipelines and AI-enabled data workflows, including support for Generative AI initiatives (e.g., content generation, vector search, model training pipelines) or strong motivation to learn and lead in this space.
Excellent communication skills in English, with the ability to clearly document and explain architectural decisions to technical and non-technical audiences.
Fast learner with strong multitasking abilities; capable of managing several cross-functional initiatives simultaneously.
Willingness to work on-site in Ashkelon once a week.
Advantage:
Experience leading POCs and tool selection processes.
Familiarity with Databricks, LLM pipelines, or vector databases is a strong plus.

Requirements:
Minimum Qualifications
PhD degree in Computer Science, Computer Engineering, Computer Graphics, Applied Sciences, Mathematics, Physics, or related field.
Experience in Python and PyTorch.
Track record of publishing papers in high-quality journals and conferences such as CVPR, ECCV/ICCV, SIGGRAPH, NIPS.
1+ years of work experience in a university or industry, in a role with primary emphasis on AI research
Preferred Qualifications
Experience with generative AI / neural rendering models.
Experience with affective computing and behaviour understanding.
Experiences in the creation and processing of large audio-visual datasets.
Experiences in solving complex problems and comparing alternative solutions, trade-offs, and different perspectives to determine a path forward.

Requirements:
MSc in Computer Science, Engineering, or a related field with a focus on AI, ML, or DS.
5+ years of experience in DS roles, with a strong emphasis on building and deploying machine learning models in production environments.
Expertise in Python and SQL.
Experience with machine learning frameworks such as TensorFlow or PyTorch.
Familiarity with cloud platforms (e.g., AWS, GCP) and containerization tools (e.g., Docker, Kubernetes).
Proven experience in developing, fine-tuning, and deploying ML models, particularly in NLP.
Ability to translate complex business problems into data-driven solutions.
Excellent verbal and written communication skills, with the ability to convey complex technical concepts to non-technical stakeholders.
Preferred Qualifications:
Background in academic or industrial research in AI.
Familiarity with the e-commerce domain and related data challenges.
Previous experience leading projects in a DS capacity.

Requirements:
2+ years of experience in operations, project management, automation and process optimization
Strong analytical and problem-solving skills with experience in project management
Ability to balance between strategy and execution- business mindset that can see the big picture together with detailed tracking and execution (tasks, risks and timelines)
Experience collaborating with cross-functional teams, including product and engineering
Familiarity with third-party AI tools integration into workflows
Knowledge of AI automation trends and a proactive approach to improving processes
Great communication skills to convey AI solutions to both technical and non-technical teams
Working with Monday-Must.

Requirements:
4+ years of product management experience, ideally with a focus on AI/ML, NLP, or infrastructure/platform products.
Strong technical background in ML infrastructure, data systems, or engineering;
Experience building or scaling systems based on large language models or unstructured data processing.
Strong understanding of the AI/LLM ecosystem - including model deployment, evaluation, prompt orchestration, and data pipelines.
Proven track record of leading cross-functional teams to deliver technically complex products with measurable impact.
Excellent communication and collaboration skills, especially in technical environments.
Ability to operate in ambiguity and drive clarity through structured thinking and sharp execution.
Background in ML infrastructure, AI products, or applied research is a plus.

Requirements:
10+ years of hands-on experience in backend development, with deep expertise in Java / TypeScript / Python and a strong grasp of software engineering best practices.
A proven track record of leading the design and implementation of complex, scalable, and high-performance systems in production environments.
Architectural experienceyouve made critical design decisions, defined technical direction, and guided teams through system evolution.
Strong understanding of distributed systems, RESTful APIs, databases, message queues, multi-tenancy, and cloud-native computing.
Experience building and evolving microservices-based architectures, ideally deployed in Kubernetes or similar container orchestration platforms.
A collaborative, communicative approachyoure comfortable influencing across teams, mentoring engineers, and driving alignment.
A proactive, ownership-driven attitudeyou seek out opportunities, solve problems before they escalate, and take initiative to improve systems and processes.
Advantages:
Experience with GenAI implementations, LLM integrations, Vector Databases (e.g., Mongo Atlas Search, Elastic Search), and embedding-based search.
Background in Cybersecurity, data privacy, or compliance-related domains, especially with experience designing secure-by-default systems.
Experience evaluating and integrating third-party APIs, SDKs, or cloud-native AI services (e.g., Azure OpenAI, AWS Bedrock, Google Vertex AI).

Requirements:
+4 years of experience as an AI and/or prompt engineer (OpenAI API, Anthropic Claude, etc.).
Proven experience building AI agents using frameworks like LangChain, AutoGPT, or custom stacks
Proficiency in Python and AI frameworks (PyTorch, LlamaIndex).
Deep understanding of tool use, memory, and autonomous task design for agents
Familiarity with embedding models and vector databases (FAISS, Pinecone, Weaviate).
Experience working with NLP pipelines, tokenization, and text preprocessing.
Strong analytical and problem-solving skills.
Strong business acumen with a proven ability to translate strategic objectives into actionable initiatives.
Preferred Qualifications:
Experience with cloud platforms (AWS, GCP, Azure) for AI deployment.
Previous experience integrating AI into production systems
Knowledge of MLOps best practices and CI/CD pipelines for AI models.
MSc Machine Learning & Data Science.

Requirements:
3-5 years of experience in ML Engineering, AI models deployment or MLOps roles.
Hands-on experience optimizing AI models for real-time or large-scale applications.
Strong proficiency with ML frameworks (TensorFlow, PyTorch, Scikit-Learn, etc.)
Production-level coding skills in Python, Java, or other languages used in AI/ML deployment.
Experience deploying AI solutions in cloud environments (AWS, GCP, or Azure) and
working with containerized infrastructure (Kubernetes, Docker).
Excellent analytical and problem-solving abilities, with a focus on model optimization, performance tuning, and long-term reliability.
Nice to have
Experience working with vector databases and generative AI APIs (e.g., OpenAI,
Anthropic).
Familiarity with MLOps practices, including automated model training pipelines, CI/CD for
AI, and real-time inference systems.
Understanding of AI governance, compliance, security, and responsible AI principles.

Requirements:
36 years of experience in applied ML, NLP, or AI system development.
Strong Python development skills and experience with LLM agent frameworks like LangChain, LlamaIndex, or similar.
Proven experience building agentic systems, including memory management, tool use workflows, and goal-directed execution.
Familiarity with semantic search systems and prompt optimization strategies for retrieval-augmented generation.
Experience working with APIs for platforms like Slack, Jira, Outlook, Salesforce, or Confluence.
Good understanding of secure system design, including permissioning, SSO/OAuth2, and compliance-aware data handling.
Preferred Qualifications
Hands-on experience with Model Context Protocol (MCP) for structured, declarative LLM-to-tool integration.
Experience evaluating agentic and RAG-based systems using task success metrics, tool invocation fidelity, memory consistency, and multi-hop reasoning quality
Exposure to Agent-to-Agent (A2A) protocols for agent coordination, role-based delegation, and inter-agent messaging.
Prior experience with enterprise AI assistants, internal copilots, or intelligent automation tools.
Familiarity with deploying AI systems using Docker, CI/CD pipelines, and cloud infrastructure (AWS, GCP, Azure).

Requirements:
Proficient in Python - at least 5 years of production-grade code development-
strong familiarity with LLM tooling frameworks (LangChain, LlamaIndex, Haystack, etc)
Is involved in AI communities and surveys the landscape for new AI tools, technologies, papers, and projects
Familiar with state-of-the-art RAG technologies
Has a deep understanding of the state-of-the-art AI pipelines and technologies
Has built and deployed AI-driven pipelines or applications into production - Must
Understand modular pipelines and CI/CD integrations.

Requirements:
5+ years of experience as a Software Engineer
Experienced in designing, developing, and debugging complex, distributed systems (microservices, event-driven)
Proven hands-on experience in containerized environments, microservices and Kubernetes
Experienced with at least one of the main cloud provider platforms (e.g. AWS, GCP)
Experience with designing, structuring, and maintaining Python SDKs
Familiarity with AI application development frameworks and tools (e.g. LangChain, OpenAI, Hugging Face, MLflow)
Experienced in Java and Python
Bonus Points:
Experience working with AI Agents and LLM-based application frameworks
Background in data engineering, feature stores, or vector databases for AI workloads
Previous experience working in developer-first companies or on AI-powered solutions.