Knowledge Graph Traversal Engineer

• Languages & Core Runtimes: Python (primary backend/data structures)
• Graph Databases & Algorithms: Neo4j, Memgraph, NetworkX, or custom graph structures; Graph algorithms (BFS/DFS variations, Dijkstra, shortest path, centrality).
• Agentic Frameworks & State Management: LangGraph, CrewAI, LlamaIndex, or custom state machines; Mem0-style persistent memory layers.
• LLMs & Inference Engine: OpenAI API, Anthropic Claude, vLLM / Ollama for local SLM/LLM inference execution.
• Testing & Infrastructure: PyTest, benchmark test harnesses, Docker, CI/CD pipelines for regression testing.

• Stateful Traversal: Design and implement graph traversal that carries state and context forward across steps driven by user intent, avoiding reasoning restarts on each turn.
• Context Modeling: Model the mathematics of context building during traversal—defining what to retain, what to prune, and how to weight paths given current intent and evidence.
• State & Memory Optimization: Bridge the memory-and-logic gap so multi-turn diagnostic reasoning remains coherent, stateful, and fast enough for near real-time interaction.
• Latency Reduction: Optimize traversal speed to achieve low-latency performance suitable for live, interactive environments.
• Quality Assurance: Introduce a robust regression test harness to ensure algorithmic updates or alternative approaches do not break established diagnostic behaviors.
• Iterative Execution: Collaborate in tight, low-overhead loops with leadership, translating verbal directions into structured, testable code.

• Ownership Culture: Strong self-starter who thrives in an autonomous, outcome-oriented work environment.
• Remote-Native Communication: Clear, proactive communication across enterprise communication channels (Slack, Teams, Jira).
• Collaborative Spirit: A pragmatic team player who values team success, constructive code reviews, and shared ownership.

• Graph Algorithms: Strong, demonstrable experience with knowledge graphs and graph traversal algorithms (beyond standard graph database queries).
• Context & State Math: Practical understanding of building and maintaining state during traversal based on user intent.
• End-to-End Delivery: Proven ability to independently drive complex workstreams from concept to deployment.
• LLM & Reasoning Systems: Experience integrating LLMs into agentic or reasoning architectures with a clear understanding of statelessness and latency constraints.
• Modern Agent Memory: Hands-on familiarity with advanced agent memory/context design patterns (e.g., Mem0-style architectures) beyond naive full-history prompting.
• Testing & Quality: Experience setting up regression testing frameworks to maintain stability in fast-paced development cycles.

• Exposure to root cause analysis, diagnostic frameworks, or escalation engineering domains (e.g., automated support, code analysis, debugging engines).
• Interest or experience with Small Language Models (SLMs) and converting raw documentation into question-answer training pairs.
• Background in reasoning-heavy systems where execution logic, timing, and state matter more than static world knowledge.

• A practical design discussion focused on traversing an existing diagnostic knowledge graph using memory and intent tracking.
• Review of prior projects where you took an ambiguous problem to a production-grade implementation independently.
• Evaluation of your approach to balancing latency, state trade-offs, and regression testing.

Graph Algorithms Neo4j / Graph Database PyTest / Regression Testing Python