AI Solutions Architect

Key Responsibilities

Solution Architecting:

• Partner with customer technical leads, customer executives, and partners to manage and deliver successful implementations of Graph+GenAI solutions becoming a trusted advisor to decision-makers throughout the engagement.
• Propose solution architectures and manage the deployment of Graph+GenAI solutions according to complex customer requirements and implementation best practices.
• Engage directly with customers to understand their business objectives and translate them into AI-powered solutions that leverage graph databases and LLMs. This includes gathering requirements, understanding existing data landscapes, and identifying opportunities to apply graph-based AI to solve business challenges.
• Interact with customer stakeholders to manage project scope, priorities, deliverables, risks and issues, and timelines for successful customer outcomes.

Solution Engineering

• Develop, test, and deploy production-ready AI applications that integrate graph databases with LLMs and orchestration frameworks. This involves writing production-level code, optimising 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

• Collaborate 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.
• Share your expertise internally with other Neo4j teams and also with customers through workshops, training sessions, and documentation to empower them to effectively utilise, 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 Development: 5+ years of experience in designing and developing enterprise-class applications, demonstrating a strong understanding of software development lifecycle principles.
• LLM Proficiency: 2+ years of 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 (e.g., OpenAI's GPT and O families, Anthropic's Claude family, Google's Gemini, xAI's Grok, as well as open-source LLMs like LLama, DeepSeek, and Mistral).
• Programming Proficiency: Competence and hands-on experience in at least one of the following languages: Java, JavaScript, Python, or C#. Ability to write clean, maintainable, and efficient code is essential.
• Deployment and Version Control: Hands-on experience with deployment software on major platforms, such as Linux, Docker, and Kubernetes, and proficiency in source control software, including Git and SVN.
• Cloud Computing Expertise: Practical experience with cloud platforms (e.g., AWS, Azure, GCP) and demonstrated proficiency in deploying applications within cloud environments.
• Generative AI Ecosystem Knowledge: Deep understanding of the generative AI ecosystem, including AI orchestration frameworks (e.g., LangChain, Llama Index, Haystack) and cloud provider AI offerings (e.g., AWS Bedrock, Vertex AI, Azure Machine Learning).
• Data Expertise: Strong foundation in data engineering, data analytics, or data science, with the ability to work effectively with various data types and sources. Experience using big data technologies (e.g. Hadoop, Spark, Hive) and database management systems (e.g. SQL and NoSQL).
• Graph Database Expertise: Deep understanding of graph database concepts, data modeling, and query languages (e.g., Cypher). Demonstrate hands-on experience with graph databases (e.g., Neo4j, Neptune, TigerGraph) or triple stores (e.g., Ontotext, Stardog).
• Communication and Collaboration Skills: Excellent communication and interpersonal skills to effectively collaborate with customers and internal teams, fostering strong working relationships.
• Problem-Solving and Analytical Abilities: Strong analytical and problem-solving abilities to address complex technical challenges and design effective AI solutions.
• You will have the ability to travel for customer engagements.