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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 maintaining AI agent systems, with a strong focus on LLM infrastructure optimization, modular tool development, and data-driven insights. Proficient in TypeScript and Python, with a solid understanding of agent tooling ecosystems and security engineering.
Highest-signal resume keywords
5+ Years Software/Platform Engineering2+ Years LLM/AI Agent Systems ExperienceTypeScript FluencyPython FluencyOpenClaw Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI Agent SystemsModular Tool DevelopmentCLI ToolingToken ManagementModel RoutingContext CompactionCost OptimizationLog AnalysisStatistical ProfilingSQL
Soft Skills
Analytical ThinkingCollaborationProblem-Solving
Tools & Technologies
OpenClawLangGraphAutoGenCrewAIKubernetesAWS Bedrock
Industry Keywords
AI-Assisted DevelopmentAgent Tooling EcosystemTelemetry InsightsAgent WorkflowsSecurity Engineering
Tech Stack
Tools & technologiesAWSKubernetesPythonSQLTypeScript
About the role
Key responsibilities & impact- Build, publish, and maintain OpenClaw skills used by hundreds of agents
- Develop CLI tooling for agent deployment, diagnostics, session management, skill registry, and developer workflows
- Own AI agent harness engineering, including lifecycle management, tool execution, context/session tuning, compaction strategies, and model routing
- Instrument the agent fleet with data pipelines and dashboards
- Analyze token efficiency, failure modes, latency distribution, and business outcome correlation
- Identify platform bottlenecks and improve agent throughput, response quality, and cost efficiency
- Track research, open-source releases, and community developments and prototype integrations
- Optimize LLM infrastructure, including token budgeting, multi-provider routing, cost attribution, and context window management
- Harden agent sandboxes through credential isolation, prompt injection defense, and guardrails
- Partner with product and business teams to translate user growth goals into reliable, scalable agent workflows
Requirements
What you’ll need- 5+ years in software/platform engineering
- 2+ years hands-on with LLM or AI agent systems in production
- AI-assisted development mindset and familiarity with agent/tool/context primitives
- Experience building modular, composable tools or CLI utilities for developer platforms
- TypeScript and/or Python fluency
- Practical experience with OpenClaw, LangGraph, AutoGen, CrewAI, or equivalent orchestration runtimes
- Experience with token management, model routing, context compaction, and cost optimization at scale
- Comfortable with log analysis, statistical profiling, and SQL/Python for usage data
- Ability to translate telemetry into actionable platform insights
- Awareness of model releases, agent framework updates, and relevant literature
- Experience shipping quickly with AI-assisted workflows and strong engineering fundamentals
- Nice-to-have: OpenClaw experience, CLI and agent tooling ecosystem familiarity, LiteLLM/AWS Bedrock/multi-provider proxy experience, Kubernetes/EKS, and security engineering experience
Benefits
Comp & perks- Competitive salary and company benefits
- Work-from-home arrangement (the arrangement may vary depending on the work nature of the business team)
- Opportunities for career growth and continuous learning
- Collaboration with world-class talent in a user-centric global organization
- Autonomy in an innovative, results-driven environment