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Requirements:
Minimum 4 years of experience as a DevOps Engineer.
Minimum 3 years of experience managing and leading a DevOps team (5+ team members).
Experience building CI/CD pipelines using tools such as GitLab, Azure DevOps, or Jenkins.
Experience with configuration management using Git.
Scripting experience in Bash / Python.
Experience working with Linux / Windows environments.
Experience with Docker containers.
Experience with Kubernetes infrastructure (familiarity with OpenShift - an advantage).
Experience working with HELM.
Experience with IaC-based configuration management tools such as Ansible / Terraform - an advantage.
Familiarity with the DevSecOps world - an advantage.
Experience working with public (AWS) and private cloud environments - an advantage.

Requirements:
requirements:
minimum 4 years of hands-on DevOps /sre experience
kubernetes in production: you've deployed it, upgraded it, and debugged networking on it
gcp: gke, cloud sql, bigquery, secret manager, iam, vpc networking
infrastructure as code with terragrunt and opentofu (terraform experience counts)
ci/cd depth: good knowledge of github actions, and gitops on kubernetes with argo cd, flux, or equivalent
dataops: airflow, dbt, and a warehouse like bigquery. you've operated pipelines in production, not only written them
monitoring and alerting with datadog, grafana, or similar
comfortable scripting and reading code in go, Python, or typescript
a pulse on what's actually breaking in production right now
someone who thrives with responsibility, moves fast, and wants to build something big
nice to have:
bazel: you've managed the build of a big monorepo
crossplane
istio or another service mesh in production
temporal or another durable workflow engine
helm chart authoring and kubernetes operators
supply-chain security tooling: trivy, gitleaks, sbom

Requirements:
Ideally, were looking for:
2+ years of hands-on experience in DevOps / Platform / SRE roles, operating distributed production systems at high scale, with a proven ability to lead projects independently and drive new implementations end-to-end
Deep hands-on expertise with AWS in production (EKS, ECS, S3, EC2, CloudFront, IAM, Bedrock, MSK, Lambda, Cloudwatch) including Infrastructure as Code (Terraform or similar)
Deep hands-on expertise with Kubernetes and Helm, including a strong understanding of how Kubernetes internals work and how to operate them in production, alongside Docker / containerized systems
Strong knowledge of networking and operating systems (TCP/IP, DNS, load balancing, Linux internals)
Strong experience with CI/CD and automation (GitHub Actions), monitoring and observability (metrics, tracing, logging, tools like Prometheus / Grafana)
High proficiency with current AI coding tools, including a deep understanding of how they work and how to apply them to drive real productivity.
These would also be nice:
Experience integrating or migrating systems between different stacks or organizations
Experience driving cross-team technical initiatives
Experience working with GPUs for inference and serving AI/ML workloads.

Requirements:
5+ years of experience in DevOps, DevSecOps, or Platform Engineering.
Strong Linux experience and scripting or development skills in Python or Go.
Production experience with AWS, EKS, and Kubernetes.
Hands-on experience securing cloud and Kubernetes environments.
Experience with Terraform and infrastructure-as-code practices.
3+ years of experience with CI/CD processes and tools, preferably GitHub Actions.
Strong knowledge of IAM, networking, secrets management, and workload security.
Experience with vulnerability management and cloud security tools such as Wiz.
Experience with Datadog, OpenTelemetry, log analytics, and distributed tracing.
Experience designing and maintaining highly available, large-scale SaaS environments.
Experience handling production and security incidents.
Strong ownership, collaboration, and customer-centric approach.

Requirements:
A seasoned DevOps / Infrastructure engineer (5+ years) with strong hands-on experience in production cloud environments.
Proven expertise operating large-scale, distributed systems - with deep understanding of Kubernetes, networking, and cloud-native architecture.
Strong experience with multi-cloud environments (AWS and/or GCP), Infrastructure-as-Code (Terraform), and GitOps workflows (ArgoCD, Flux, or similar).
Hands-on experience with CI/CD systems (Jenkins, GitHub Actions, etc.).
Solid scripting and automation skills (Python, Bash, or Go).
Proven track record of being a collaborative team player who partners closely with developers, ML engineers, and cross-functional stakeholders across the organization.
Experience with observability stacks (Prometheus, Grafana, OpenTelemetry, Logz.io, or similar).
Experience with databases (relational and/or NoSQL) - including operational aspects like backups, migrations, and performance tuning.
AI-Native Engineering: You are an AI-native engineer who leverages LLMs and agentic tools (like Cursor, Copilot, or Claude) not just for command completion, but as a core operational partner - automating diagnostics, runbooks, and infrastructure workflows so you can focus on the critical things.

Requirements:
Your Experience
5-8 years of progressive experience in DevOps, managing high-scale systems and mission-critical production environments.
A strong sense of ownership with a proven track record of taking tasks end-to-end (from implementation and QA to production) while managing cloud costs and resources effectively.
Extensive experience designing, scaling, and managing complex Kubernetes clusters and containerized workloads, with deep architectural expertise in GCP.
Proven experience executing production upgrades at scale with a rigorous focus on system reliability and minimal disruption.
Strong internal collaboration skills, with a focus on unblocking peers, leading technical deep-dives, and fostering a cohesive, high-trust environment within the DevOps squad and beyond.
Expert-level proficiency in Linux system architecture, advanced network engineering, and automation development (Python, Go, or Bash).
Mastery of Infrastructure as Code (IaC) and configuration management using Terraform, Ansible, and Helm to drive immutable infrastructure at scale.
Proven track record of designing secure, high-velocity CI/CD pipelines from scratch using GitLab CI, Jenkins, or similar enterprise tooling.
Hands-on experience working with modern monitoring and observability solutions, as well as managing distributed datastores at scale (BigQuery, MongoDB, Neo4j, or ElasticSearch).
Practical experience or a strong technical interest in working with AI tools, AI initiatives, and implementing Agentic solutions.
Demonstrated leadership ability with a history of driving architectural decisions, mentoring engineers, and supporting complex, multi-service production environments.

Requirements:
Required Skills and Experience
Bachelors degree or higher in Computer Science, Software Engineering, EE, Information Systems, or a related field
Minimum 8+ years of experience as a Dataflow and DevOps development, Backend Engineer, Data Engineer, or similar role
Strong experience with Python
Strong SQL skills, including query optimization, indexing, stored procedures, and performance tuning
Experience with ETL/ELT processes, data pipelines, and integration between systems
Strong experience with automation scripting language (ex: Groovy), data processing, and tooling
Experience with Docker, Kubernetes, Jenkins and CI/CD frameworks
Familiar with developing in Linux and Windows environments
Experience with backup, restore, migration, monitoring, and database maintenance processes
Understanding of data security, permissions, access control, and data integrity practices
Experience with Git, Jira, and Agile development workflows
High English skills: listening, speaking, and reading (C1-2)
Nice to have:
Past experience with CloudBees, Antifactory, and integrating them into a secure system
Strong experience with relational databases such as PostgreSQL and Pgvector, MySQL, and NoSQL
Experience with cloud database services such as AWS RDS, Azure SQL, Google Cloud SQL, or similar
Experience with database schema design, data modelling, and database normalization
Experience with AI agents and AI automation
Familiarity with database replication, clustering, high availability, and disaster recovery
Experience with BI/reporting tools or data visualization platforms
Experience with backup solutions and backup strategies
Experience working in an R&D, semiconductor, embedded systems, or hardware/software engineering environment
Experience and background in MAL, datasets, and MAL Python libraries.
Experience in JAVA.

Requirements:
At least 7+ years of hands-on experience in DevOps in large-scale infrastructure companies.
Passion for elegant and effective technological solutions.
Independent learning capability and curiosity without boundaries.
Strong analytical and problem-solving skills.
Proven ability to lead complex technical projects independently.
Technical requirements:
Advanced experience with Kubernetes and container orchestration.
Strong proficiency with IaC Frameworks such as Terraform and Terragrunt, CDK
Solid hands-on experience with AWS services.
Strong understanding of network fundamentals.
Experience with GitOps tools (ArgoCD)
Experience with advanced CI/CD pipelines.

Requirements:
3-8+ years of hands-on experience in DevOps with strong AWS expertise.
Proficiency in CI/CD pipelines (GitHub Actions, Jenkins, AWS CodePipeline), IaC (Terraform, CloudFormation), and containerization (Docker, Kubernetes).
Experience modifying Dockerfiles and optimizing build processes based on AWS Inspector results.
Experience monitoring and remediating findings from AWS Security Hub.
Strong scripting skills (Python, Bash, PowerShell) and GitHub repository management.

Requirements:
6+ years of hands-on DevOps / Platform Engineering experience in large-scale production environments on a public cloud (AWS preferred).
Proven leadership mindset - able to own projects end to end and be accountable for outcomes.
Strong, production-grade Kubernetes experience across design, deployment, scaling, and troubleshooting.
Solid AWS experience with VPC, IAM, EC2, EKS, Load Balancers, and DNS.
Experience designing and operating highly available, scalable infrastructure systems.
Experience with managed and distributed databases (AWS Aurora, RDS, MongoDB, Redis).
Hands-on experience with Infrastructure as Code and configuration management (Terraform required; Terragrunt and Ansible a plus).
Experience with Docker and containerized workloads.
2+ years building and maintaining CI/CD pipelines (Jenkins, GitHub Actions)
Experience with monitoring and observability tools (Prometheus, Grafana).
Experience with GenAI platforms (AWS Bedrock, Vertex AI, OpenAI).

Requirements:
5+ years of experience as a DevOps Engineer in complex production environments.
3+ years of hands-on cloud experience, preferably with AWS, with strong knowledge of services such as EKS, EC2, S3, RDS, ElastiCache, SQS, VPC, and Route 53.
Proven experience designing secure, scalable, and highly available cloud architectures, including multi-account environments.
3+ years of hands-on Kubernetes experience, including Helm, operators, and production-grade cluster management.
Strong experience with CI/CD and GitOps practices using ArgoCD and/or GitHub Actions. Experience with Argo Workflows is a strong advantage.
3+ years of hands-on experience with Terraform.
Strong scripting and automation skills using Python and/or Bash.
Experience executing infrastructure and DevOps projects end-to-end in enterprise-scale environments.
Extensive experience using AI-powered development and engineering tools such as Claude Code, Codex, Cursor, or similar tools to improve productivity, automation, troubleshooting, and software delivery.
Excellent problem-solving, communication, and collaboration skills.
Strong ownership, hands-on approach, and ability to operate independently.
Passion for continuous learning, innovation, knowledge sharing, and mentoring others.

Requirements:
2+ years of hands-on experience in DevOps / Platform / SRE roles, operating distributed production systems at large scale, with a proven ability to lead projects independently and drive new implementations end-to-end
2+ years of experience working with cloud Inferencing services and NeoClouds
2+ years of hands-on experience building and maintaining AI Factory systems on KubeFlow/Ray/similar.
2+ years of hands on experience with GPU Inferencing (vLLM/SGLang/Nvidia Triton)
Deep hands-on expertise with multicloud AWS/GCP/NeoClouds (RunPod, TogetherAI, vast.ai) including Infrastructure as Code (Terraform or similar)
Deep hands-on expertise with Kubernetes and Helm, including a strong understanding of how Kubernetes internals work and how to operate them in production, alongside Docker / containerized systems
Strong knowledge of networking and operating systems (TCP/IP, DNS, load balancing, Linux internals)
Strong experience with CI/CD and automation (GitHub Actions), monitoring and observability (metrics, tracing, logging, tools like Prometheus / Grafana)
High proficiency with current AI coding tools, including a deep understanding of how they work and how to apply them to drive real productivity.
These would also be nice:
Familiarity with AI evals and AI observability system, such as Opik, OpenEval, LangSmith etc

Requirements:
Your Experience:
4+ years operating production infrastructure in AWS.
Deep hands-on experience with Kubernetes, Helm, ArgoCD, Terraform, and CI/CD.
Strong experience with observability and alerting in Datadog or comparable platforms.
Solid grounding in Linux, networking, cloud security, and reliability best practices.
Strong scripting skills in Python and Bash.
Proven ability to own platform projects end-to-end, from design through production operation and ongoing improvement.
Strong troubleshooting across distributed systems, Kubernetes, CI/CD, and live incidents.
Collaborative mindset - comfortable working across engineering, security, product, and leadership.
Comfort in a fast-paced, high-ownership environment where priorities shift but production quality doesn't.
Genuine interest in applying AI, automation, and intelligent workflows to operational work.
Key qualities
Ownership-driven - You take responsibility for the systems you build and operate, from design through production support and continuous improvement.
Collaboration - You work effectively across engineering, security, product, and leadership to align priorities and drive shared outcomes.
Developer experience focus - You are committed to reducing friction for engineering teams through thoughtful automation, self-service workflows, and reliable internal tooling.
Innovation balanced with pragmatism - You actively explore new approaches, particularly in AI-assisted operations, while weighing them against reliability, maintainability, and operational simplicity.
Security mindset - You design and build with least privilege, auditability, and production safety as foundational principles rather than afterthoughts.
Clear communication - You articulate infrastructure, reliability, cost, and security tradeoffs precisely to both technical and non-technical stakeholders.

Requirements:
Your Experience
10+ years of progressive experience in DevOps, SRE, Platform, or Infrastructure Engineering roles, with a significant portion at the principal/ tech leadership/ staff, or architectural level.
System Design from Scratch: A proven track record of designing, building, and deploying large-scale, highly available distributed systems and cloud platforms from the ground up.
AI-Powered Automation: Proven experience designing and integrating AI-driven systems, autonomous agents, and LLM-based tools into engineering workflows to optimize development processes, procedures, and overall organizational efficiency.
Communication: Exceptional interpersonal skills, capable of articulating complex architectural and AI workflow concepts clearly to both deeply technical peers and executive leadership.
Cloud & IaC Mastery: Expert-level proficiency with GCP (or equivalent major cloud providers) and deep architectural experience with Terraform.
Advanced Container Orchestration: Deep, internal knowledge of virtualized and containerized environments, with architectural-level expertise in scaling Kubernetes, extending it via custom operators, and automating complex operational logic.
Software Engineering Approach: Advanced coding and automation skills in Python or Go. You treat infrastructure as a software engineering discipline and can build custom tooling/services when off-the-shelf solutions fall short.
Proven Leadership: Demonstrated ability to lead complex, cross-team technical initiatives from conception to delivery, including setting technical roadmaps and driving consensus among stakeholders.
OS/Systems Expertise: Mastery of Linux systems, including kernel tuning, advanced networking, and performance troubleshooting.
Nice to Have:
Deep expertise in managing and scaling stateful workloads and distributed databases (e.g., Cassandra, ScyllaDB, MemSQL, or MySQL) in containerized environments.
Experience contributing to open-source infrastructure projects, or presenting at major tech conferences.

Requirements:
5+ years of professional DevOps or infrastructure engineering experience
Strong expertise in CI/CD pipeline design and implementation (GitHub Actions, Jenkins, or equivalent)
Familiarity with automotive development processes (AUTOSAR, ISO 26262, OTA deployment)
Proficiency with Docker, containerization, and modern deployment practices
Experience with Kubernetes or container orchestration platforms
Experience with infrastructure-as-code (Terraform, CloudFormation, or similar)
Solid understanding of cloud platforms (AWS, GCP, Oracle or Azure) and networking fundamentals
Proficiency with monitoring, logging, and observability tools (Prometheus, Grafana, ELK, or equivalent)
Strong scripting and programming skills (Python, Bash, Go, or equivalent)
Available to work on-site in Tel Aviv, Israel