Job Title: AI Solutions Architect

Job Summary: As aAI Solutions Architect, you will lead the design, construction, and deployment of AI solutions that combine the power of graph databases and AI. You will act as a trusted advisor to our strategic customers, guiding them through complex data challenges and delivering transformative business value. You will have a great understanding of graph technologies, LLMs, and AI frameworks, and will be able to translate customer needs into production-ready AI applications grounded in real-world context using Neo4j Knowledge Graphs.

Key Responsibilities:

Solution Architecting: 

• You will engage with technical leaders, stakeholders, and strategic partners to shape and guide the successful implementation of Graph+GenAI solutions, serving as a trusted advisor and thought partner to decision-makers throughout complex, high-impact engagements. 
• You will design and advocate for robust, scalable solution architectures, leading the deployment of Graph+GenAI systems aligned with complex enterprise requirements and industry best practices driving architectural excellence and long-term value.
• You will collaborate closely with customer leadership to deeply understand their strategic objectives, translating them into high-impact AI solutions that harness the synergy of graph databases and LLMs. This includes leading the discovery of requirements, assessing enterprise data ecosystems, and identifying innovative opportunities to apply graph-based AI at scale.
• You will lead stakeholder engagement across all levels to steer project scope, align on priorities, mitigate risks, and ensure timely delivery—ensuring alignment between technical execution and business outcomes while maintaining a focus on measurable impact

Solution Engineering: 

• You will develop, test, and deploy production-ready AI applications that integrate graph databases with LLMs and orchestration frameworks. This involves writing production-level code, optimizing for performance and scalability, and ensuring seamless integration with customer systems.
• Continuously evaluate and improve the performance, scalability, and efficiency of deployed AI applications, incorporating new techniques and technologies as they emerge.

Education & Enablement:

• You will work with other teams at Neo4j (Product and Marketing) to influence the roadmap and provide insights from the field, and package approaches, best practices, and lessons learned into thought leadership, methodologies, and published assets.
• You will share your expertise internally with other Neo4j teams and also with customers through workshops, training sessions, and documentation to empower them to effectively utilize, maintain, and reproduce the AI solutions you deliver.
• Maintain continuous learning and stay up-to-date with the rapidly evolving GenAI landscape, proactively seeking knowledge of new trends and technologies.

Required Qualifications:

• Enterprise Application Architecture: 5+ years of experience architecting and delivering enterprise-grade applications, with a deep understanding of the full software development lifecycle and the ability to guide teams through complex design and implementation decisions.
• LLM Proficiency: Experience working with Large Language Models (LLMs), including prompt engineering, fine-tuning, and integrating LLMs into applications. Maintain up-to-date knowledge of different LLM providers and their strengths and limitations, as well as open-source ones).
• Programming Proficiency: Advanced proficiency in at least one major programming language (e.g., Java, JavaScript, Python, or C#), with a proven track record of delivering clean, maintainable, and scalable code within complex systems. 
• Deployment and Version Control: Deep hands-on experience with deployment tooling across Linux, Docker, and Kubernetes environments, along with expert-level use of version control systems (e.g., Git, SVN) in enterprise development workflows.
• Cloud Computing Expertise: Demonstrated expertise in deploying and scaling applications across cloud platforms (AWS, Azure, GCP), with a strategic understanding of cloud-native architecture and DevOps best practices. 
• Generative AI Ecosystem Knowledge: In-depth knowledge of the generative AI ecosystem, including frameworks (e.g., LangChain, LlamaIndex, Haystack) and familiarity with cloud-native AI platforms (e.g., AWS Bedrock, Google Vertex AI, Azure ML), enabling you to architect end-to-end AI solutions.
• Data & Analytics Fluency: Strong background in data engineering, analytics, or data science, with the ability to design data pipelines and workflows across structured and unstructured data. Hands-on experience with big data technologies (e.g., Hadoop, Spark, Hive) and database systems (SQL and NoSQL)
• Graph Database Authority: Deep expertise in graph data modeling and query languages (e.g., Cypher), along with practical experience working with graph databases (e.g., Neo4j, Amazon Neptune, TigerGraph) or triple stores (e.g., Ontotext, Stardog), enabling advanced knowledge graph applications
• Communication and Collaboration Skills: Exceptional communication and stakeholder engagement skills, with a demonstrated ability to influence cross-functional teams and align technical decisions with business goals.
• Problem-Solving and Analytical Abilities: Strong analytical mindset with the ability to break down complex problems, architect AI solutions, and mentor teams through implementation
• Willingness and ability to travel up to 50% to engage with customers, lead strategic discussions and ensure successful project execution.