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Requirements:
3+ years of hands-on experience with cloud-native technologies such as containers and Kubernetes (required).
Minimum three years of experience in customer-facing roles such as technical sales or sales/solutions engineering (required).
Track record of success in a technical pre-sales role, with enough experience selling and implementing technology to earn customer trust.
Demonstrable ability to articulate and sell the benefits of modern platforms, software, and technologies.

Requirements:
A Foundation in Engineering Leadership: 10+ years of technology experience, including a significant background in software engineering, solutions architecture, and DevSecOps/DevOps/SRE, with a deep understanding of how modern applications are designed, built, and deployed.
People Management Experience: 3+ years directly managing or formally leading technical teams, ideally including senior architects or principal-level engineers, with a track record of hiring, developing, and retaining strong technical talent.
Financial Services / Multi-Entity Experience: Prior experience providing security advisory in regulated environments, multi-entity corporate structures, or financial technology platforms is preferred.
Deep Application Security Expertise: 5+ years in application, product, or cloud security architecture, with the ability to evaluate and guide architectural mitigations for OWASP Top 10 and design-level vulnerabilities in a microservices architecture.
Cloud Security Architecture: Demonstrated experience defining architecture patterns and conducting security assessments in cloud-native platforms, specifically Google Cloud Platform (GCP); familiarity with AWS or Azure is beneficial.
Expertise in Securing Distributed Systems: Strong understanding of security design patterns for microservices architectures, containerized environments (Kubernetes), and event-driven systems, including API gateways, service mesh concepts, secrets management, and network policies.
Pragmatic Threat Modeling Expertise: Proven background leading and overseeing architectural threat modeling exercises (e.g., STRIDE or similar frameworks) across cloud-native and distributed environments, and translating theoretical threats into actionable architecture guardrails and engineering requirements.
Emerging Technologies: Familiarity with secure architecture considerations for Generative AI integration, agentic workflows, and modern data platform architectures.
Excellent Communication and Influence: Exceptional ability to articulate complex technical risks and solutions to both senior engineers and executive stakeholders to drive consensus and prioritization.
Budget and Vendor Management: Experience managing team budgets and evaluating security tooling or vendor relationships is a plus.
Relevant security, application security, and security architecture certifications (e.g., CISSP, CSSLP, GIAC) are beneficial.

Requirements:
5+ years of experience as a software engineer or data scientist building production systems.
2+ years of experience with large language models and NLP techniques
Background in developing AI agents for content heavy tasks.
Experience with multiple LLM Vendors, RAG or Fine Tuning OSS or Commercial models.
Technical Sills:
Proficiency in Python and machine learning libraries such as TensorFlow or PyTorch.
Deep understanding of LLM architectures and NLP applications like entity recognition, document classification, and summarization.
Experience with model grounding techniques, retrieval-augmented generation (RAG), and deploying robust AI models in production environments.
Skills in designing and deploying AI models through APIs, containerization (Docker, Kubernetes), and cloud platforms (AWS, GCP, Azure).
Understanding of best practices for continuous monitoring, evaluation, and iterative improvement of deployed models.

Requirements:
5+ years of experience in software engineering with a focus on AI-driven or autonomous systems.
Proven track record of deploying AI models or agents in production environments.
Technical Skills:
Proficiency in Python.
Databases: Proficiency in SQL (PostgreSQL, MySQL) and NoSQL (e.g. Document DB, Vector DB).
Cloud: Experience deploying large scale production applications on AWS, GCP, or Azure.
Security: Understanding of OAuth2, JWT, and best practices for securing systems.

Requirements:
5+ years of experience as a frontend, fullstack, or software engineer.
Proven experience building production-grade web applications, preferaby in AI-driven or data-intensive environments.
Experience working in early-stage startup environments is a plus.
Technical Skills
Frontend
Proficiency in modern frontend frameworks such as React, Vue, Svelte, or Angular (bonus for React or Svelte).
Strong skills in TypeScript, JavaScript, HTML, CSS.
Experience with UI/UX best practices and responsive design.
Backend
Experience building APIs using Node.js Strong understanding of REST/GraphQL API design.
Experience with SQL (PostgreSQL, MySQL)
Experience with CI/CD pipelines for automated testing and deployment.

Requirements:
5+ years of experience in backend or infrastructure engineering, ideally supporting production AI/ML systems or high-throughput data pipelines.
Proven track record of building and scaling infrastructure in production environments.
Technical Skills:
Proficiency in Python.
Databases: Proficiency in SQL (PostgreSQL, MySQL) and NoSQL (e.g. Document DB, Vector DB).
Cloud: Deep experience deploying and scaling large production applications on AWS, GCP, or Azure.
Containerization and orchestration: Docker, Kubernetes.
Security: Strong understanding of OAuth2, JWT, and best practices for securing distributed systems.

Requirements:
7+ years of experience in Implementation Engineering, Solutions Engineering, Technical Consulting, Professional Services or a similar post-sale customer-facing role.
Experience in the enterprise segment, specifically within the Israeli market, preferably in traditional industries such as financial services, insurance, and banking.
Strong analytical skills with the ability to translate business needs into technical solutions.
Strong understanding of AI concepts and their enterprise applications.
High adoption of modern AI tools and workflows.
Experience supporting deployments across cloud environments and enterprise system integrations.
Excellent communication and stakeholder management skills.
Demonstrated ability to manage solution delivery across the full post-sale customer lifecycle
Excellent Hebrew proficiency (daily client communications are in Hebrew)

Requirements:
Bachelors degree in Software Engineering / Computer Engineering.
3-4 Years Hands-on experience in system testing of complex systems.
Strong understanding of system architecture and multidisciplinary environments.
Experience in requirement analysis and test design.
Experience writing formal test documentation.
Experience with test and defect management tools (e.g., TFS, Jira).
Knowledge of communication protocols (e.g., TCP/IP, REST API, WebSocket, SNMP).
Familiarity with network monitoring and performance testing tools.
Understanding of software testing methodologies and SDLC.
Good English (reading and writing).
Strong analytical thinking and problem-solving skills.
Ability to work independently and collaborate with multiple stakeholders.

Requirements:
Requirements:
5+ years of hands-on experience in product-based tech companies, with a focus on AI/ML systems.
Proven track record in designing and implementing GenAI-powered solutions using LLMs and transformer-based architectures.
Deep understanding of prompt engineering, RAG (Retrieval-Augmented Generation), fine-tuning, and LLM-based workflows.
Strong experience with Azure Cognitive Services.
Proficiency in Python or other relevant languages (e.g., TypeScript, Go), including use of AI/ML frameworks (e.g., LangChain, Hugging Face, PyTorch).
Familiarity with containerized environments and orchestration tools (e.g., Docker, Kubernetes).
Comfortable working with multi-cloud environments (Azure, AWS, or GCP).
Experience with Agile/Scrum development methodologies.
Strong communication skills and fluency in English (spoken and written).

Preferred Qualifications/ Skills
Experience with building high-performance distributed systems.
Familiarity with agentic frameworks for multi-step reasoning or workflow automation.
Exposure to AI model governance, data compliance, and MLOps pipelines.
Background in Computer Science or related technical degree.