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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in building and integrating AI platforms, focusing on LLM-powered applications and multi-agent systems. Proficient in leveraging tools like LangChain and Kubernetes to enhance development workflows and ensure safe agent execution.
Highest-signal resume keywords
LLM-Powered Applications DevelopmentKubernetes Infrastructure IntegrationLangChain and OpenAI API ExperienceAI Observability and MonitoringAsynchronous Backend Services 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
PythonTypeScriptFastAPIAI Agent ArchitecturePrompt EngineeringMCP Server IntegrationRegression TestingAI Guardrails ImplementationDockerHelm
Soft Skills
Critical ThinkingAnalytical SkillsTroubleshootingCross-Functional Collaboration
Tools & Technologies
JenkinsGitHub ActionsAWSAzureInfrastructure as CodeGitOps
Industry Keywords
AI PlatformsMulti-Agent SystemsAgentic ApplicationsGovernance PracticesError Recovery
Tech Stack
Tools & technologiesAWSAzureCloudDockerJenkinsKubernetesPythonTypeScript
About the role
Key responsibilities & impact- Design and build internal AI platforms, reusable SDKs, libraries, tools, and MCP servers for agentic applications
- Develop single- and multi-agent workflows covering planning, tool selection, delegation, state and memory management, execution, retries, fallbacks, error recovery, and human-in-the-loop approval
- Integrate AI capabilities into Kubernetes infrastructure and Jenkins/GitHub Actions pipelines to improve development, testing, deployment, and operational workflows
- Implement agent tracing, prompt and tool-call logging, latency and error monitoring, token and cost tracking, regression testing, and failure analysis
- Apply guardrails, access controls, auditability, and governance practices to ensure agents operate safely and reliably
- Own AI platform capabilities from architecture through production
- Build reusable solutions that accelerate adoption across engineering teams
- Collaborate with engineering and business teams to identify high-impact AI opportunities and translate them into scalable technical solutions
- Evaluate emerging AI frameworks, models, and cloud technologies and promote adoption where they provide measurable value
Requirements
What you’ll need- Minimum of 2 years of hands-on production experience building LLM-powered applications, AI agents, multi-agent systems, or internal AI platforms
- Hands-on experience with LangChain, LangGraph, Claude Agent SDK, and OpenAI and/or Anthropic APIs
- Strong understanding of agent architecture, including planning, tool selection, delegation, state and memory management, retries, fallbacks, error recovery, and human-in-the-loop workflows
- Experience with prompt and context engineering, tool/function calling, and structured outputs
- Experience building and integrating MCP servers and clients, including tool schemas, permissions, reliable execution, and error handling
- Experience implementing AI evaluations and regression tests measuring agent quality, task completion, tool usage, behavioral changes, and failure modes
- Experience with AI observability, including traces, prompts, tool calls, latency, errors, token consumption, costs, and production debugging
- Understanding of AI guardrails, security controls, auditability, governance, and safe agent execution
- Ability to build reusable AI platform capabilities that accelerate adoption across engineering teams
- Critical thinking and ability to apply sound judgment to AI inputs
- Minimum of 5 years of hands-on development experience with Python and/or TypeScript
- Experience building asynchronous backend services and APIs using FastAPI or an equivalent modern framework
- Strong software engineering, analytical, troubleshooting, and cross-functional collaboration skills
- Ability to build reliable, reusable internal platforms that improve developer productivity and accelerate technology adoption
- Strong experience with Kubernetes and Helm, including deployment, networking, resource management, autoscaling, troubleshooting, chart development, templating, versioning, and release management
- Strong experience with Docker, Jenkins, GitHub Actions, and AWS and/or Azure
- Familiarity with Infrastructure as Code, GitOps, and Git-based collaborative workflows
Benefits
Comp & perks- Full-time employment
- Hybrid work arrangement in Tel Aviv
- Opportunity to work on GenAI, agentic AI, fraud prevention, and behavioral biometrics technologies
- Opportunity to build reusable AI platforms and solutions adopted across engineering teams
- Cross-functional collaboration with engineering and business teams
- Opportunity to evaluate and adopt emerging AI frameworks, models, and cloud technologies