Principal AI Solutions Architect
New
N
Neo4jGraph databases and AI
Remote: United StatesFull-TimePrincipal
SalaryAnnual On Target Earnings Range $220,000 — $310,000 USD
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Job Details
- Experience
- 10+ years of experience architecting and delivering enterprise-grade applications; 5+ years of experience working with Large Language Models (LLMs).
- Required Skills
- AWSDockerPythonJavaJavascriptKubernetesC#Prompt Engineering
Requirements
- 10+ years of experience architecting and delivering enterprise-grade applications, with understanding of the full software development lifecycle.
- 5+ years of experience working with LLMs, including prompt engineering, fine-tuning, and application integration.
- Advanced proficiency in at least one major programming language, such as Java, JavaScript, Python, or C#.
- Hands-on deployment experience across Linux, Docker, and Kubernetes, and expert-level use of version control systems such as Git or SVN.
- Experience deploying and scaling applications on AWS, Azure, or GCP, with knowledge of cloud-native architecture and DevOps best practices.
- Knowledge of generative AI frameworks such as LangChain, LlamaIndex, or Haystack and cloud-native AI platforms such as AWS Bedrock, Google Vertex AI, or Azure ML.
- Background in data engineering, analytics, or data science, including designing pipelines and workflows for structured and unstructured data.
- Hands-on experience with big data technologies such as Hadoop, Spark, or Hive, and SQL and NoSQL databases.
- Expertise in graph data modeling and query languages such as Cypher, with experience using graph databases or triple stores.
- Willingness and ability to travel up to 50% to engage with customers and support project execution.
Responsibilities
- Engage with technical leaders, stakeholders, and strategic partners to guide Graph+GenAI implementations.
- Design scalable solution architectures and lead deployment of Graph+GenAI systems for enterprise requirements.
- Discover customer requirements, assess enterprise data ecosystems, and identify opportunities for graph-based AI.
- Lead stakeholder engagement to define scope, align priorities, mitigate risks, and support timely delivery.
- Develop, test, and deploy production-ready AI applications integrating graph databases, LLMs, and orchestration frameworks.
- Evaluate and improve application performance, scalability, and efficiency.
- Work with Product and Marketing teams to share field insights and influence the roadmap.
- Share expertise through customer and internal workshops, training, documentation, methodologies, and published assets.
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