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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in designing and implementing AI agents and workflows, with a strong focus on LLM applications, data synthesis across various database types, and optimization of agentic frameworks. Proven ability to mentor teams and apply robust development practices in cloud-native environments.
Highest-signal resume keywords
AI Agent DevelopmentFull Stack DevelopmentGraph Database Query LanguagesCloud-Native DeploymentPrompt Engineering
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
LLM ApplicationsData SynthesisAgentic FrameworksMemory Management StrategiesPerformance TuningRAG ArchitecturesCI/CD PracticesSource-Control PracticesContext-Window OptimizationMulti-Turn State Handling
Soft Skills
MentoringTechnical Direction
Tools & Technologies
MongoDBLangChainLangGraphRedisOpenShiftKubernetesModerne
Industry Keywords
Agentic AI SolutionsGraph DatabasesVector DatabasesDocument DatabasesRelational Databases
Tech Stack
Tools & technologiesCloudKubernetesMongoDBOpenShiftRedis
About the role
Key responsibilities & impact- Design and ship intelligent agents, agentic workflows, and LLM-powered capabilities across graph, vector, and document databases
- Design, build, and ship AI agents and agentic workflows into production environments
- Synthesize and reconcile data across graph, document (MongoDB), and vector data sources
- Develop memory and context management strategies for LLM applications, including context-window optimization and multi-turn state handling
- Engineer reliable and bounded agent iteration and self-correction loops
- Build and optimize RAG architectures using LangChain and LangGraph
- Apply graph database query languages, data modeling, and performance tuning to support agentic reasoning at scale
- Design and implement agents using Agent Core or comparable agentic frameworks
- Optimize prompt engineering and LLM interaction patterns for cost, latency, and output quality
- Apply robust testing, CI/CD, and source-control practices to AI system development
Requirements
What you’ll need- 5+ years of experience required; 7+ years preferred
- 5 years’ experience with Full Stack Development
- Experience with multiple database types: graph, vector, document, and/or relational databases
- Experience building Agentic AI Solutions preferred
- Experience with MCP (Model Context Protocol), CrewAI, or similar emerging agentic frameworks preferred
- Familiarity with Redis for caching, session state, or short-term memory implementations preferred
- Experience with cloud-native deployment on OpenShift or Kubernetes preferred
- Exposure to code modernization tooling such as Moderne preferred
- Full-stack development experience preferred
- Prior experience mentoring or setting technical direction for a team preferred
- University (Degree) preferred
Benefits
Comp & perks- Superior retirement program
- Competitive health benefits
- Wellness offerings
- Work life offerings
- Meaningful learning experiences and development pathways
- Incentive program linked to performance, depending on the role (e.g., annual discretionary incentive programs, non-annual sales incentive plans, or other non-annual incentive plans)