Sr. AI Solutions Architect

Role overview A senior individual contributor role focused on designing and delivering production-grade AI solutions that combine graph database technology with large language models. The position acts as a trusted technical advisor to enterprise customers, translating complex data challenges into high-impact, context-aware applications while collaborating across engineering, product, and customer success functions.

Responsibilities - Partner with technical leaders, stakeholders, and strategic accounts to shape and guide implementations that fuse graph technology with generative AI. - Design scalable, resilient solution architectures aligned with enterprise requirements and long-term operational goals. - Translate strategic customer objectives into concrete AI solutions by leading discovery, assessing data ecosystems, and identifying graph-based opportunities at scale. - Build, test, and deploy production-ready AI applications integrating graph databases, LLMs, and orchestration frameworks, with attention to performance and scalability. - Continuously evaluate deployed applications and incorporate emerging techniques to improve efficiency and reliability. - Develop enablement materials, workshops, and documentation that empower internal teams and customers to maintain and reproduce delivered solutions.

Requirements - 7+ years architecting and delivering enterprise-grade applications across the full software development lifecycle. - 2+ years of hands-on experience with large language models, including prompt engineering, fine-tuning, and integration. - Advanced proficiency in at least one major programming language such as Java, JavaScript, Python, or C#. - Deep experience with deployment tooling across Linux, Docker, and Kubernetes, plus expert-level use of Git or similar version control systems. - Demonstrated expertise deploying and scaling applications on AWS, Azure, or GCP with a strong grasp of cloud-native architecture. - In-depth familiarity with the generative AI ecosystem, including vector databases, embedding strategies, and retrieval-augmented generation patterns.

Nice to have - Experience shaping product roadmaps through field insights and contributing to thought-leadership content such as blogs, talks, or technical papers.