Senior AI Solutions Architect

Role Overview

Lead the design, construction, and deployment of production-ready AI solutions that integrate graph databases with LLMs and AI orchestration frameworks. Act as a trusted advisor to strategic customers, translating complex data challenges into real business value.

Key Missions

• Engage technical leaders, stakeholders, and strategic partners to shape and guide successful Graph + GenAI implementations.
• Develop, test, and deploy end-to-end AI applications combining graph databases, LLMs, and orchestration frameworks.
• Collaborate with other teams to influence product/engineering roadmaps and share field insights, best practices, and lessons learned.

Responsibilities

• Architect and deliver enterprise-grade solutions across the software development lifecycle.
• Mentor teams through implementation decisions and support scalable, maintainable engineering.
• Deploy and scale solutions across cloud environments and ensure reliable production delivery.

Requirements

• Programming: Advanced proficiency in at least one major language (Java, JavaScript, Python, or C#) with a track record of clean, scalable code.
• LLMs: 2+ years working with Large Language Models, including prompt engineering, fine-tuning, and LLM integration.
• Graph expertise: Deep knowledge of graph data modeling and query languages such as Cypher, plus hands-on experience with graph databases (Neo4j, Amazon Neptune, TigerGraph) or triple stores (Ontotext, Stardog).
• Generative AI ecosystem: In-depth knowledge of genAI frameworks such as LangChain, LlamaIndex, Haystack and cloud-native AI platforms like AWS Bedrock, Google Vertex AI, Azure ML.
• Architecture & problem solving: Strong analytical skills to break down complex problems and architect solutions; ability to mentor.
• Enterprise architecture: 7+ years architecting and delivering enterprise-grade applications.
• Cloud & DevOps: Experience deploying and scaling across AWS, Azure, GCP, plus DevOps best practices.
• Deployment & version control: Hands-on experience with Linux, Docker, Kubernetes and strong version control (e.g., Git, SVN).
• Data & analytics: Experience in data engineering/analytics/data science; ability to design pipelines across structured/unstructured data; familiarity with Hadoop, Spark, Hive and SQL/NoSQL.
• Travel: Willingness to travel up to 50% to engage customers and lead discussions.
• Communication: Exceptional stakeholder management and influence across cross-functional teams.

Nice-to-haves

• Experience with knowledge graph and triple-store implementations.
• Broad familiarity across multiple LLM providers and open-source LLMs.