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Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in architecting and implementing scalable multi-agent systems, with a strong focus on LLMOps and AgentOps best practices. Proficient in Python and TypeScript, with extensive experience in cloud architecture and security risk mitigation for AI systems.
Highest-signal resume keywords
Expert-Level Proficiency In PythonExperience With LLM APIsDeep Understanding Of Agentic Design PatternsStrong Background In AWS, GCP, Or AzureExperience With Docker/Kubernetes Containerization
Tech Stack
Tools & technologiesAbout the role
Key responsibilities & impact- Architect, design, and implement scalable multi-agent systems automating complex, multi-step business processes
- Translate existing processes and develop novel multi-agent architectures for new opportunities
- Implement agentic solutions using orchestration frameworks such as Google ADK, LangGraph, and CrewAI
- Design secure tool-calling architectures enabling LLMs to safely interact with internal databases, CRMs, and APIs
- Implement LLMOps and AgentOps best practices
- Mitigate AI-specific security risks, including prompt injection, hallucination loops, and unauthorized tool execution
- Optimize data pipelines that provide context to AI systems
- Develop agents and the APIs/tools they use
- Meet with clients, stakeholders, or off-site personnel/management as needed
- Demonstrate commitment to diversity, equity, and inclusion
Requirements
What you’ll need- Ph.D. in Computer Science, Engineering, Mathematics, Physics, Artificial Intelligence, Software Engineering, or a closely related field
- 1–7+ years of software engineering experience
- Expert-level proficiency in Python and/or TypeScript
- Experience with MCP architectures
- Proficiency in Rust
- Deep understanding of agentic design patterns, including ReAct, Plan-and-Solve, Reflection, and Tree of Thoughts
- Extensive experience with LLM APIs, including OpenAI, Anthropic, and Google Gemini
- Experience with open-weights models such as Llama 3 and Mistral
- Experience with vector and graph databases
- Experience with RAG architectures, including GraphRAG and hybrid search
- Strong background in AWS, GCP, or Azure cloud architecture
- Experience with Docker/Kubernetes containerization
- Preferred: enterprise-scale deployment of multi-agent architectures
- Preferred: expert-level proficiency in Rust
- Preferred: 3+ years specifically in generative AI, LLMs, or cognitive architectures
- Preferred: extensive RAG architecture experience
- Ability to work 40 hours per week, Monday to Friday
- Some travel may be required
Benefits
Comp & perks- Competitive salary
- Benefits package
- Remote work option
- Reasonable accommodation during the hiring process
- Equal opportunity employment
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonTypeScriptRustMCP ArchitecturesAgentic Design PatternsLLM APIsVector DatabasesGraph DatabasesRAG ArchitecturesData Pipeline Optimization
Soft Skills
Client EngagementCommitment To Diversity, Equity, And Inclusion
Tools & Technologies
Google ADKLangGraphCrewAIDockerKubernetes
Certifications & Qualifications
Ph.D. In Computer SciencePh.D. In EngineeringPh.D. In MathematicsPh.D. In PhysicsPh.D. In Artificial IntelligencePh.D. In Software Engineering
Industry Keywords
Multi-Agent SystemsAI-Specific Security RisksOrchestration FrameworksEnterprise-Scale DeploymentGenerative AI