DevOps Tech Lead

Description

At Lusha, we’re building for builders. We build fast and AI-first, so we look for builders. By a builder, we mean someone who turns “maybe” into “done”.

We’re looking for a hands-on DevOps Tech Lead to shape how Lusha builds and operates infrastructure in the AI era. You’ll lead the design and development of a shared agentic platform, giving engineering teams the foundations to build, deploy, and operate agents in production. You’ll also evolve our AWS and Kubernetes platform and bring AI into the way we develop, deliver, and operate software.

A central part of your role will be designing reusable infrastructure and services for agent execution, tool and MCP integrations, model access, and operational visibility. You’ll partner with R&D, Security, and Data Platform to turn AI experiments into supported production capabilities, and adapt CI/CD, observability, and infrastructure workflows for AI-assisted and agent-driven development.

You’ll define engineering standards for the platform: workload isolation, identity and secrets, scoped tool access, evaluation gates, deployment and rollback, and traceability of agent actions. You’ll mentor engineers and guide architecture and code reviews, balancing autonomy with appropriate approvals, reliability, and cost controls.

Experience with AI tools is a must. You’ll use AI coding tools daily and build AI into how the DevOps group works: infrastructure changes, incident investigation, documentation, and developer self-service. You’ll own the quality of the outcome and measure improvements in delivery speed, operational effort, and reliability.

The position is based in Tel Aviv, with a hybrid model of three office days per week.

This might be for you if:

• You enjoy building shared platforms that developers choose to use because they make shipping software and agents faster, simpler, and safer.
• You enjoy designing AWS and Kubernetes infrastructure for both services and agent workloads, including isolation, networking, identity, scaling, and recovery.
• You have strong experience with microservices orchestration across polyglot environments at scale.
• You care about agent and service observability: execution traces, tool calls, failures, latency, model usage, and cost, alongside incident response and recovery.
• You enjoy mentoring engineers and helping teams make clear technical decisions through thoughtful reviews and practical documentation.
• You evaluate new technologies and balance delivery speed, reliability, security, performance, and costs when choosing solutions.
• You want to build agent-driven DevOps workflows with scoped permissions, audit trails, appropriate approval points, and reliable recovery paths.

Requirements

• 7+ years of hands-on experience in DevOps, SRE, or Platform Engineering, with demonstrated experience leading technical initiatives and mentoring engineers.
• Ability to design infrastructure for agent execution, including asynchronous jobs, retries, state, concurrency, and failure recovery, and to reason about the risks of agents using tools and accessing data.
• Deep production experience with AWS, including EKS, IAM, VPC networking, load balancing, and core compute and storage services.
• Strong Kubernetes, Docker, and Helm expertise, including cluster operations and troubleshooting distributed applications.
• Proven experience designing and maintaining Infrastructure as Code with Terraform or OpenTofu; familiarity with Terragrunt.
• Experience operating production SaaS environments and building CI/CD pipelines and GitOps workflows, using tools such as GitHub Actions and Argo CD.
• Strong Python programming skills, with experience developing, extending, and maintaining automation and internal DevOps tools.
• Solid Linux and networking fundamentals, including DNS, TLS, HTTP, and connectivity troubleshooting.
• Experience with production incidents, observability, rollback strategies, and recovery procedures.
• Practical understanding of least-privilege identity, secrets management, workload isolation, and controlled tool access for services and AI agents.
• Experience building shared agent platforms or internal developer platforms adopted by multiple teams.
• Ability to prioritize work across teams, drive initiatives through to adoption, and explain technical decisions clearly to technical and non-technical stakeholders.
• Hands-on experience building or integrating AI agents, LLM applications, or MCP tools into engineering workflows, with concrete examples of what you shipped and how you validated it.
• Regular use of AI coding tools to deliver reviewed, tested, and maintainable code.

Nice to have:

• Familiarity with Vault, Karpenter, OpenTelemetry, Prometheus, Grafana, or Coralogix.
• Experience improving cloud efficiency through capacity planning, resource optimization, and FinOps practices.
• Experience with agent runtimes and orchestration, MCP servers, model gateways, or evaluation frameworks; familiarity with model routing, quotas, and usage controls.
• Familiarity with infrastructure supporting Databricks and data workloads, and experience partnering with Data Platform teams.
• Familiarity with the AWS Well-Architected Framework.