לוח משרות דרושים מהנדסים בתל אביב יפו

Requirements:
Bachelor's degree in Computer Science or equivalent practical experience.
At least 3 years of experience in data engineering or related roles.
Experience with big data Machine Learning - a must!.
Proficiency in Python- must. Scala is a plus.
Experience with Big Data technologies including Spark, EMR and Airflow.
Experience with containerization/orchestration platforms such as Docker and Kubernetes.
Familiarity with distributed computing on the cloud (such as AWS or GCP).
Strong problem-solving skills and ability to learn new technologies quickly.
Being goal-driven and efficient.
Excellent communication skills and ability to work independently and in a team.

Requirements:
Mandatory Skills:

5+ years of experience in software engineering, with strong proficiency in Python.

Experience with LangGraph, LangChain, or similar frameworks for building AI agents.

Strong understanding of LLMs, prompt engineering, and AI agent architectures.

Familiarity with REST APIs and integrating external data sources.

Preferred Skills:

Familiarity with the AI Agents ecosystem, including different LLM providers, vector store solutions, MCPs, and more.

Experience with evaluating LLM performance and quality.

Familiarity with cloud environments, especially AWS.

Requirements:
Minimum Qualifications:

A PhD in computer science or related fields OR Masters with 1 year of related industry experience OR Bachelors with 2 years of related industry experience (or equivalent coursework/research experience).
Strong programming skills in generic programming languages such as Python or Golang.

Experience in one or more of the following areas:
Designing and building scalable, reliable, and secure backend infrastructure (e.g., distributed systems, cloud services, data pipelines, APIs) for large-scale applications.
Developing and deploying machine learning or deep learning models, with practical application of core concepts such as bias and variance, supervised and unsupervised learning, and Generative AI.

Preferred Qualifications:

Comfortable with fast, iterative development cycles in an environment that requires autonomous thinking, risk taking and bias for action.

Ability to use the latest GenAI technologies and methodologies for software development workflows.

Familiarity with ML frameworks like PyTorch.

Excellent written and verbal communication skills, strong analytical and problem-solving skills.

Fluency in reading academic papers on AI/ML and security and the ability to translate their ideas into prototype or production systems.

Requirements:
Minimum Qualifications:

A PhD in computer science or related fields with 5 years of industry or academic experience in artificial intelligence, OR Masters with 7 years of related industry experience OR Bachelors with 10 years of related industry experience.
Strong programming skills in generic programming languages such as Python.

Experience in one or more of the following areas:
Designing and building scalable, reliable, and secure backend infrastructure (e.g., distributed systems, cloud services, data pipelines, APIs) for large-scale applications.
A strong background in AI, machine learning, and deep learning technologies, with a solid understanding of core ML concepts such as bias and variance, supervised and unsupervised learning, and Generative AI.
Developing, training, fine-tuning, or evaluating AI/ML models, algorithms, or platforms (including deep learning, reinforcement learning, generative models, etc.).

Preferred Qualifications:

Comfortable with fast, iterative development cycles in an environment that requires autonomous thinking, risk taking and bias for action.
Ability to use the latest GenAI technologies and methodologies for software development workflows.
Familiarity with ML frameworks like PyTorch.
Excellent written and verbal communication skills, strong analytical and problem-solving skills.
Fluency in reading academic papers on AI/ML and security and the ability to translate their ideas into prototype or production systems.

Requirements:
Minimum Qualifications

5+ years of professional experience in software engineering, data engineering, or product ownership roles.
Strong development experience with Python or Golang (both advantage).
Demonstrated ability to translate business requirements into technical roadmaps and deliverables.
Experience working in Agile/Scrum environments and managing product backlogs.
Experience with orchestration and containerization (Kubernetes, Docker).
Familiarity with Linux and cloud infrastructure (especially AWS).
Preferred Qualifications

Bachelors or masters degree in computer science, Engineering, or related field.
Familiarity with data warehousing and modern data stack technologies.
Proven hands-on experience with Apache Airflow, and Apache Spark.
Knowledge of message queues and real-time systems (RabbitMQ, Redis, Kinesis).
Knowledge of DevOps and CI/CD tools.
Strong stakeholder management and communication skills.
Ability to balance technical depth with strategic vision.

Requirements:
Minimum Qualifications:

A PhD in computer science or related fields OR Masters with 2 years of related industry experience OR Bachelors with 4 years of related industry experience.
Strong programming skills in generic programming languages such as Python or Golang.

Experience in one or more of the following areas:
Designing and building scalable, reliable, and secure backend infrastructure (e.g., distributed systems, cloud services, data pipelines, APIs) for large-scale applications.
AI, machine learning, and deep learning technologies, with a solid understanding of core ML concepts such as bias and variance, supervised and unsupervised learning, and Generative AI.

Preferred Qualifications:

Comfortable with fast, iterative development cycles in an environment that requires autonomous thinking, risk taking and bias for action.
Ability to use the latest GenAI technologies and methodologies for software development workflows.
Familiarity with ML frameworks like PyTorch.
Excellent written and verbal communication skills, strong analytical and problem-solving skills.
Fluency in reading academic papers on AI/ML and security and the ability to translate their ideas into prototype or production systems.