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Requirements:
3+ years experience in SecOps / Cloud Security / Security Engineering
Hands-on experience securing Google Cloud Platform environments
Strong understanding of:
IAM (roles, service accounts, workload identity)
Network security (VPCs, firewall rules, load balancers)
Organization policies and security baselines
Experience operating and tuning security controls in production
Detection, Monitoring & Response:
Experience with Google Cloud Security Command Center
Experience with Google SecOps (Chronicle) or equivalent SIEM
Building and tuning:
Detection rules
Alerts
Dashboards
Incident triage, investigation, and response in cloud environments
Log ingestion and normalization from cloud and third-party sources
Automation & Engineering:
Strong scripting skills (Python preferred)
Security automation (SOAR-like workflows, custom tooling)
Experience integrating security tooling via APIs
CI/CD security controls (shift-left, pipeline security checks)
Cloud & Platform Security
Experience securing:
GKE
Cloud Run
Compute Engine
Knowledge of container security concepts (images, registries, runtime)
Vulnerability management and remediation workflows
Governance & Compliance:
Experience with security posture management
Familiarity with common frameworks (ISO 27001, SOC 2, CIS Benchmarks)
Risk assessment and security findings remediation
Nice to Have:
Experience with Wiz, Prisma Cloud, or similar CSPM tools
Experience with threat modeling and attack simulations
Experience working with regulated environments
Google Cloud security certifications

Requirements:
3+ years of experience as a Data Engineer
Strong hands-on experience with Google Cloud Platform
Advanced SQL (BigQuery performance, partitioning, clustering)
Strong Python (data pipelines, APIs, batch & streaming jobs)
Experience building batch and streaming pipelines
BigQuery
Dataflow / Apache Beam
Pub/Sub
Cloud Storage
Data modeling for analytics and ML use cases
Production troubleshooting, optimization, and cost awareness
ML & GenAI"
Experience supporting ML pipelines (training, inference, feature prep)
Hands-on with at least one of:
Vertex AI
Feature stores
Model inference pipelines
Experience working with LLM-based systems, including:
Embeddings generation
Vector databases / similarity search
RAG-style pipelines
Ability to prepare, version, and serve data for GenAI / ML workloads
Platform & Engineering:
Experience with Airflow / Cloud Composer or equivalent orchestration
CI/CD pipelines for data & ML workloads
Infrastructure-as-code familiarity (Terraform preferred)
Understanding of IAM, data security, and access controls on GCP
Nice to Have:
Experience with FinOps / cost optimization
Experience with real-time ML or low-latency inference
Experience with monitoring & observability for data/ML pipelines
Google Cloud certifications (Data Engineer / Architect)

Requirements:
5+ years of relevant experience:
Direct client sales/management.
Experience with Cloud technology - A big pro!
Understanding of Big Data landscape and Business Intelligence technologies, as well as knowledge of how various cloud technologies (PaaS and IaaS) work together to drive deeper technology adoption within the existing large account base.
Demonstrated ability to achieve strategic goals in a fast-paced environment.
Ability to engage with both developer audiences as well as C-level IT and business leaders.
Bachelors degree or equivalent practical experience.
Ability to speak and write in Hebrew and English fluently

Requirements:
M.Sc. in Computer Science, Electrical Engineering, or related technical field
3+ years of experience in developing Computer Vision algorithms
2+ experience leading an algorithm development team
Strong Skills in statistical analysis, mathematical, and problem-solving skills
Ability to quickly learn new technologies and techniques
Independent, creative, and possess initiative
Experience with the automotive industry - advantage