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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive experience in DevOps and Platform Engineering, with a strong focus on designing and maintaining infrastructure for AI-assisted workflows. Proven ability to mentor engineers, lead technical initiatives, and implement best practices for operational efficiency and reliability.
Highest-signal resume keywords
AWS ExpertiseKubernetes ProficiencyInfrastructure As Code (Terraform)Python Programming SkillsCI/CD Pipeline Development
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
DevOpsPlatform EngineeringInfrastructure DesignAI IntegrationAsynchronous Job ManagementLoad BalancingSecrets ManagementObservabilityCapacity PlanningResource Optimization
Soft Skills
MentoringTechnical LeadershipCross-Team CollaborationClear Communication
Tools & Technologies
DockerHelmGitHub ActionsArgo CDVaultKarpenterOpenTelemetryPrometheusGrafanaCoralogix
Industry Keywords
SaaSAI AgentsMCP ToolsGitOpsWell-Architected Framework
Tech Stack
Tools & technologiesAWSCloudDNSDockerGrafanaKubernetesLinuxPrometheusPythonTerraformVault
About the role
Key responsibilities & impact- Design and develop a shared agentic platform for engineering teams
- Provide infrastructure and services for agent execution, tool and MCP integrations, model access, and operational visibility
- Evolve Lusha’s AWS and Kubernetes platform
- Bring AI into software development, delivery, and operations workflows
- Partner with R&D, Security, and Data Platform to turn AI experiments into supported production capabilities
- Adapt CI/CD, observability, and infrastructure workflows for AI-assisted and agent-driven development
- Define engineering standards for workload isolation, identity and secrets, scoped tool access, evaluation gates, deployment and rollback, and traceability of agent actions
- Mentor engineers and guide architecture and code reviews
- Balance autonomy with appropriate approvals, reliability, and cost controls
- Use AI coding tools daily and build AI into DevOps workflows, including infrastructure changes, incident investigation, documentation, and developer self-service
- Own outcome quality and measure improvements in delivery speed, operational effort, and reliability
- Evaluate new technologies and balance delivery speed, reliability, security, performance, and costs
- Prioritize work across teams and drive initiatives through adoption
Requirements
What you’ll need- 7+ years of hands-on experience in DevOps, SRE, or Platform Engineering
- Demonstrated experience leading technical initiatives and mentoring engineers
- Ability to design infrastructure for agent execution, including asynchronous jobs, retries, state, concurrency, and failure recovery
- Ability to reason about 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
- 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
Benefits
Comp & perks- Hybrid work model of three office days per week
- AI coding tools used daily
- Mentoring and technical growth through architecture and code reviews
- Opportunity to build shared platforms and agent-driven DevOps workflows
- Opportunity to work with AWS, Kubernetes, AI agents, LLM applications, and MCP tools