AI Solutions Architect

Neo4j

. Engage with technical leaders, stakeholders, and strategic partners to guide implementation of Graph+GenAI solutions .

Posted 11/2/2026full-timeRemote • United StatesMid-LevelSenior💰 $160,000 - $220,000 per yearWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in architecting and delivering enterprise-grade AI applications, with a strong focus on integrating Large Language Models and graph databases. Proven ability to lead stakeholder engagement, design scalable solutions, and mentor teams through complex implementations.

Highest-signal resume keywords

Graph Database ExpertiseLarge Language Model IntegrationCloud-Native ArchitectureAI Application DeploymentStakeholder Engagement

ATS Keywords

Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills

JavaJavaScriptPythonC#LinuxDockerKubernetesHadoopSparkSQL

Soft Skills

Exceptional CommunicationAnalytical Problem-SolvingInfluencing Cross-Functional Teams

Tools & Technologies

AWSAzureGCPNeo4jAmazon NeptuneTigerGraphLangChainLlamaIndexHaystackGit

Industry Keywords

Generative AIData EngineeringData ScienceDevOps Best PracticesCloud-Native AI Platforms

Tech Stack

Tools & technologies

AWSAzureCloudDockerGoogle Cloud PlatformHadoopJavaJavaScriptKubernetesLinuxNeo4jNoSQLPythonSparkSQLSubversion

About the role

Key responsibilities & impact

Engage with technical leaders, stakeholders, and strategic partners to guide implementation of Graph+GenAI solutions
Serve as a trusted advisor and thought partner to decision-makers during complex, high-impact engagements
Design and advocate for robust, scalable solution architectures
Lead deployment of Graph+GenAI systems aligned with enterprise requirements and industry best practices
Collaborate with customer leadership to understand strategic objectives and translate them into AI solutions using graph databases and LLMs
Lead requirements discovery, assess enterprise data ecosystems, and identify opportunities to apply graph-based AI at scale
Lead stakeholder engagement to steer project scope, align priorities, mitigate risks, and ensure timely delivery
Develop, test, and deploy production-ready AI applications integrating graph databases with LLMs and orchestration frameworks
Write production-level code and optimize applications for performance, scalability, and customer-system integration
Continuously evaluate and improve deployed AI applications
Work with Product and Marketing teams to influence the roadmap and provide field insights
Package approaches, best practices, and lessons learned into thought leadership, methodologies, and published assets
Share expertise through workshops, training sessions, and documentation
Maintain continuous learning and stay current with the evolving GenAI landscape

Requirements

What you’ll need

5+ years of experience architecting and delivering enterprise-grade applications
Deep understanding of the full software development lifecycle
Experience working with Large Language Models, including prompt engineering, fine-tuning, and integrating LLMs into applications
Up-to-date knowledge of different LLM providers, their strengths and limitations, and open-source LLMs
Advanced proficiency in at least one major programming language, such as Java, JavaScript, Python, or C#
Proven track record of delivering clean, maintainable, and scalable code within complex systems
Deep hands-on experience with Linux, Docker, Kubernetes, and version control systems such as Git or SVN
Expert-level use of version control systems in enterprise development workflows
Demonstrated expertise deploying and scaling applications across AWS, Azure, or GCP
Strategic understanding of cloud-native architecture and DevOps best practices
In-depth knowledge of generative AI frameworks such as LangChain, LlamaIndex, or Haystack
Familiarity with cloud-native AI platforms such as AWS Bedrock, Google Vertex AI, or Azure ML
Strong background in data engineering, analytics, or data science
Ability to design data pipelines and workflows across structured and unstructured data
Hands-on experience with Hadoop, Spark, Hive, SQL, and NoSQL database systems
Deep expertise in graph data modeling and query languages such as Cypher
Practical experience with graph databases such as Neo4j, Amazon Neptune, or TigerGraph, or triple stores such as Ontotext or Stardog
Exceptional communication and stakeholder engagement skills
Ability to influence cross-functional teams and align technical decisions with business goals
Strong analytical and problem-solving abilities
Ability to architect AI solutions and mentor teams through implementation
Willingness and ability to travel up to 50%

Benefits

Comp & perks

Stock option grant
Annual bonus eligibility for certain roles
Medical benefits
Dental benefits
Vision benefits
401(k)
Paid time off
Certain leaves of absence
Inclusive, diverse, and equitable workplace
Opportunity to shape the future of data and analytics
Customer-facing workshops, training, and professional development through continuous learning