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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 reusable SDKs, libraries, and tools that enhance developer productivity. Proficient in managing Kubernetes infrastructure and implementing AI observability practices to ensure reliable and efficient agent operations.
Highest-signal resume keywords
Python DevelopmentKubernetes ManagementAI Platform IntegrationAsynchronous Backend ServicesLangChain 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
TypeScript DevelopmentFastAPI FrameworkDockerJenkinsGitHub ActionsAWSAzureAI Agent ArchitecturePrompt EngineeringMCP Server Integration
Soft Skills
Analytical SkillsTroubleshootingCross-Functional Collaboration
Tools & Technologies
HelmInfrastructure as CodeGitOpsLangGraphClaude Agent SDKOpenAI APIAnthropic API
Industry Keywords
AI PlatformsMulti-Agent SystemsAgentic ApplicationsAI ObservabilityGovernance Practices
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 and 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 their adoption where they provide measurable value
Requirements
What you’ll need- 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
- 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 that measure 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