Okay, I've searched for Senior Knowledge Graph AI Engineer roles in the United States that are 100% remote, with 7+ years of experience, expertise in RDF, OWL, SPARQL, and Neo4j, and contract-type jobs. I have found 45 positions matching your criteria.

Sure. Here's the analysis:

Job Analysis:

This Senior Knowledge Graph AI Engineer role is fundamentally about pioneering the architecture and implementation of advanced Knowledge Graphs fused with AI reasoning systems at an enterprise scale. The core objective is to build intelligent, scalable systems that can represent and reason over complex, multi-modal data, bridging structured semantic graphs with large language models and autonomous agents. The role demands not only deep technical expertise in semantic web standards (RDF, OWL, SPARQL), graph databases like Neo4j or AWS Neptune, and AI reasoning paradigms but also strong leadership in guiding cross-functional teams combining software engineers, data scientists, and domain experts. Success in this role means delivering robust inference capabilities, semantic enrichment pipelines, and AI-powered decision support tools that push forward the startup’s stealth-mode diagnostic engine vision. The challenges include navigating evolving state-of-the-art AI methodologies, managing complex data ontologies and pipeline integrations, and balancing research-level innovation with production readiness. High autonomy and cross-disciplinary judgment are expected in selecting technologies, reasoning engines, and designing hybrid AI solutions. Effective communication is vital to mentor teams and crystallize architectural strategies that gain company-wide traction.

Company Analysis:

Avesta is a niche technology solutions partner specializing in assembling and managing elite SaaS and tech product teams tailored for client needs. Given that the end customer here is a stealth-mode AI diagnostics startup, this role sits at the convergence of cutting-edge AI innovation and agile startup culture, albeit through a structured talent delivery framework that Avesta provides. Avesta’s positioning as a bespoke talent partner suggests a company culture that values excellence, adaptability, and client alignment over rigid structures. For the candidate, this means operating in an environment where technical mastery and effective cross-team collaboration are critical, but also flexibility to meet rapidly evolving startup priorities is prized. The role likely offers high visibility given the strategic nature of the customer's product, with opportunities to influence the technical trajectory. Working remotely under a contract-to-hire model implies a focus on delivering results quickly while proving cultural and technical fit. Thriving here requires both deep technical leadership and the agility to integrate into a dynamic innovation ecosystem facilitated by Avesta’s client-focused approach.

Absolutely. Here are some mock interview questions that could come up:

• Behavioral: Tell me about a time when you led the design and deployment of a complex Knowledge Graph at scale. What were the biggest challenges, and how did you overcome them?
• Behavioral: Describe an experience where you had to bridge the gap between AI research innovations and production-level systems. How did you manage cross-functional collaboration?
• Behavioral: Give an example of a situation where you mentored a team on best practices for semantic technologies. How did you ensure knowledge transfer was effective?
• Behavioral: Share an instance where you had to resolve conflicting technical opinions on architecture or tooling within a team. What approach did you take?
• Technical: How do you architect a Knowledge Graph system that supports multi-hop reasoning and real-time inference combining LLMs and structured data?
• Technical: What experiences do you have with semantic enrichment pipelines—specifically entity linking, relationship extraction, and ontology alignment? How did you implement them?
• Technical: Can you discuss your proficiency with reasoning engines like Drools, Prolog, or RDFox? How have you integrated these with AI models in past projects?
• Technical: How have you used frameworks such as LangChain or CrewAI to integrate LLMs with Knowledge Graph structures? Please describe a concrete use case.
• Situational: Imagine you discover your graph data infrastructure (e.g., AWS Neptune) has scalability bottlenecks during peak load. How would you diagnose and remediate this?
• Situational: Suppose the semantic model you designed does not adequately support domain expert queries, causing repeated misunderstandings. How would you approach refining the ontology and tooling?
• Situational: Your startup customer requires rapid prototyping of an AI diagnostic agent combining symbolic reasoning and LLMs but timeline is tight. How do you balance quality, innovation, and speed?
• Company Fit: What attracts you to working on a stealth-mode startup project via a company like Avesta rather than joining the startup directly or a large tech firm?
• Company Fit: How do you see your expertise in Knowledge Graph AI advancing the mission of a general purpose diagnostics engine? What motivates you about this domain?
• Company Fit: Avesta emphasizes building bespoke technology teams. How do you approach integrating into new teams and environments to deliver impactful results quickly?
• Questions for Interviewer: How does Avesta facilitate communication and collaboration between its engineering talent and stealth-mode startup clients, especially in complex AI projects?
• Questions for Interviewer: What are the key milestones or KPIs expected from the Senior Knowledge Graph AI Engineer within the first six months?
• Questions for Interviewer: Can you share more about the engineering culture and team structure at the customer startup — particularly how cross-functional collaboration is orchestrated remotely?