דרושים » הנדסה » Senior AI Engineer AI Cybersecurity

Requirements:
6+ years of hands-on experience in Windows development, using languages such as C#, Python, Go, Java, or Rust for system-level engineering.
Proven track record of leading development efforts in a fast-paced, results-oriented environment, including responsibility for delivering products to customers (CI/CD pipelines, packaging, and release processes).
Bonus: Background in Windows internals, process injection techniques, or sandboxing technologies.
Strong advantage: Experience in security domains such as behavioral detection, endpoint protection, or anti-tampering.
Genuine curiosity about GenAI, LLM integrations, and modern developer tooling.
A strong sense of ownership and motivation to help define and build a new product line from the ground up.

Requirements:
5+ years of backend/software engineering experience with an AI-first and builder mindset and microservices architecture.
Practical experience building GenAI agents, beyond demos.
Strong Python skills (but this role is not just Python).
Familiarity with LLMs, prompting, and orchestration frameworks.
Startup mindset: shipped to production, owned systems end-to-end.
Comfortable reading docs, research papers, and experimenting independently.
Excited to adopt new development workflows and tools.

Requirements:
Expreince leading engineering teams in fast-paced environments with strong ownership and architectural responsibility.
Backend Expertise: 6+ years of backend development experience with deep expertise in Node.js and microservices. Strong distributed systems and API design experience.
GenAI Systems Experience: Hands-on experience building production LLM systems. Proven experience with RAG architectures, vector databases, embedding pipelines, and prompt orchestration. Experience designing multi-step or agentic AI workflows.
Infrastructure: Strong experience with AWS and Kubernetes in production environments. Deep knowledge of SQL & NoSQL systems.
Communication: Ability to translate complex AI systems into clear product and business decisions.

Requirements:
5+ years of hands-on programming in Python or Go (you know one well, and youre comfortable picking up the others). Strong fundamentals in system design, performance, and debugging.
3+ years working with AWS, Kubernetes, and RDS (you know your way around cloud-native deployments, scaling, and persistence).
B.Sc. in Computer Science / Engineering or equivalent experience from a technological army unit.
Proven experience leading major projects: youve owned significant codebases or systems, taken them from design to delivery, and driven them into production.
Experience (or strong interest) in the real-world behavior of ML/DL systems, with solid understanding of transformer architectures and modern NLP frameworks - a strong advantage.
Experience deploying and scaling ML models, working with model serving frameworks, or contributing to MLOps workflows - a strong advantage.

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:
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:
3-5+ years of experience in software engineering or backend development roles.
Strong hands-on experience with one of the leading backend programming languages (such as Python, Go, Java, or Node.js), and comfortable working with Bash, CLI tools, and scripting.
Solid software engineering fundamentals and experience building and maintaining production-grade systems.
Experience designing and implementing system integrations, working with APIs, or building automation-heavy workflows.
Hands-on experience with AI services, LLMs, AI agents, or AI-driven workflows (in production or meaningful projects).
Understanding of security, data governance, and responsible AI considerations when building AI-based systems.
Strong analytical and problem-solving skills, with the ability to break down ambiguous problems into clear execution plans.
Self-driven, proactive, and comfortable operating independently in a fast-paced startup environment.
Demonstrated curiosity and continuous self-learning mindset in the AI domain.
Strong critical thinking and decision-making abilities.

Requirements:
5+ years of experience in Swift/Objective-C/C++ system programming for macOS.
Deep understanding of macOS internals, extensions, and sandbox models.
Experience with endpoint agents or network-level protection preferred.
Passion for performance, low-level debugging, and security-driven design.
Excited by AI-driven ecosystems and early-stage product ownership.

Requirements:
Extensive hands-on experience in Business Applications and/or software development
Experience building end-to-end AI solutions and automations
Deep understanding of business processes and a drive to impact business results
Experience with AI platforms (e.g., OpenAI, Claude, Gemini) and automation/orchestration tools (e.g., n8n, Make.com, LangChain)
Development capabilities, preferably in Python
Hands-on experience designing and consuming REST APIs, working with HTTP methods, authentication mechanisms, and integrating GTM platforms into AI-driven workflows.
High degree of independence and initiative - able to run projects autonomously
Sharp analytical skills and a structured problem-solving mindset
Excellent written and verbal communication skills; able to bridge technical and non-technical stakeholders
Passion for continuous learning, experimentation, and staying current with AI trends
Experience working with GTM or RevOps teams in a SAAS company - a big advantage
Experience with Salesforce - a big advantage.

Requirements:
Requirements:

3+ years of hands-on experience in software engineering, with at least 1-2 years working on generative AI projects (LLMs, diffusion models, multimodal models, etc.).

Proven ability to go from idea to production-ideally with examples of real-world AI features youve shipped.

Fluency in Python, Node.js, or similar languages used in ML and full-stack development.

Experience with prompt engineering, fine-tuning, or embedding models using frameworks like LangChain, LlamaIndex, or similar.

Familiarity with AWS services and best practices, including Lambda, S3, SageMaker, ECS/EKS, Bedrock etc.

Experience with MLOps and model deployment practices (e.g., containerization, GPU inference, vector databases).

Creativity and initiative-able to pitch and prototype ideas with minimal oversight.

Strong communication skills and the ability to explain technical concepts to non-technical stakeholders.

Nice-to-Haves:

Prior experience integrating generative AI in FinOps or cloud cost optimization tools.

Background in NLP, computer vision, or other relevant ML fields.

Contributions to open-source AI tools or research.

Knowledge of responsible AI principles and handling model risks.