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About This Role
About Neo4j:
Neo4j is the graph intelligence platform that transforms data into knowledge to power the next generation of intelligent applications and AI systems. It includes enterprise\-ready knowledge graphs for accurate, explainable, and governed AI; the most comprehensive, trusted, and easy\-to\-deploy graph capabilities across any environment and data source; and an unmatched ecosystem trusted by 84 of the Fortune 100 and supported by the world's largest graph community. Intelligence that works. Results that matter.
Built to work everywhere and integrate with everything across every cloud for dynamic, personalized, and autonomous AI systems. We deliver quicker results, contextual knowledge, and solutions that impact customers and employees across the business.
Our Vision:
At Neo4j, we have always strived to help the world make sense of data.
As business, society and knowledge become increasingly connected, our technology promotes innovation by helping organizations to find and understand data relationships. We created, drive and lead the graph database category, and we're disrupting how organizations leverage their data to innovate and stay competitive.
Job Title: Senior AI Solutions Architect
Job Summary: As a Senior AI Solutions Architect, you will lead the design, construction, and deployment of AI solutions that combine the power of graph databases and AI. You will act as a trusted advisor to our strategic customers, guiding them through complex data challenges and delivering transformative business value. You will have a great understanding of graph technologies, LLMs, and AI frameworks, and will be able to translate customer needs into production\-ready AI applications grounded in real\-world context using Neo4j Knowledge Graphs.
Key Responsibilities:
Solution Architecting:
- You will engage with technical leaders, stakeholders, and strategic partners to shape and guide the successful implementation of Graph\+GenAI solutions, serving as a trusted advisor and thought partner to decision\-makers throughout complex, high\-impact engagements.
- You will design and advocate for robust, scalable solution architectures, leading the deployment of Graph\+GenAI systems aligned with complex enterprise requirements and industry best practices driving architectural excellence and long\-term value.
- You will collaborate closely with customer leadership to deeply understand their strategic objectives, translating them into high\-impact AI solutions that harness the synergy of graph databases and LLMs. This includes leading the discovery of requirements, assessing enterprise data ecosystems, and identifying innovative opportunities to apply graph\-based AI at scale.
- You will lead stakeholder engagement across all levels to steer project scope, align on priorities, mitigate risks, and ensure timely delivery—ensuring alignment between technical execution and business outcomes while maintaining a focus on measurable impact
Solution Engineering:
- You will develop, test, and deploy production\-ready AI applications that integrate graph databases with LLMs and orchestration frameworks. This involves writing production\-level code, optimizing 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:
- You will work 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.
- You will share your expertise internally with other Neo4j teams and also with customers through workshops, training sessions, and documentation to empower them to effectively utilize, 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 Architecture: 7\+ years of experience architecting and delivering enterprise\-grade applications, with a deep understanding of the full software development lifecycle and the ability to guide teams through complex design and implementation decisions.
- 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, as well as open\-source ones).
- Programming Proficiency: Advanced proficiency in at least one major programming language (e.g., Java, JavaScript, Python, or C\#), with a proven track record of delivering clean, maintainable, and scalable code within complex systems.
- Deployment and Version Control: Deep hands\-on experience with deployment tooling across Linux, Docker, and Kubernetes environments, along with expert\-level use of version control systems (e.g., Git, SVN) in enterprise development workflows.
- Cloud Computing Expertise: Demonstrated expertise in deploying and scaling applications across cloud platforms (AWS, Azure, GCP), with a strategic understanding of cloud\-native architecture and DevOps best practices.
- Generative AI Ecosystem Knowledge: In\-depth knowledge of the generative AI ecosystem, including frameworks (e.g., LangChain, LlamaIndex, Haystack) and familiarity with cloud\-native AI platforms (e.g., AWS Bedrock, Google Vertex AI, Azure ML), enabling you to architect end\-to\-end AI solutions.
- Data \& Analytics Fluency: Strong background in data engineering, analytics, or data science, with the ability to design data pipelines and workflows across structured and unstructured data. Hands\-on experience with big data technologies (e.g., Hadoop, Spark, Hive) and database systems (SQL and NoSQL)
- Graph Database Authority: Deep expertise in graph data modeling and query languages (e.g., Cypher), along with practical experience working with graph databases (e.g., Neo4j, Amazon Neptune, TigerGraph) or triple stores (e.g., Ontotext, Stardog), enabling advanced knowledge graph applications
- Communication and Collaboration Skills: Exceptional communication and stakeholder engagement skills, with a demonstrated ability to influence cross\-functional teams and align technical decisions with business goals.
- Problem\-Solving and Analytical Abilities: Strong analytical mindset with the ability to break down complex problems, architect AI solutions, and mentor teams through implementation
- Willingness and ability to travel up to 50% to engage with customers, lead strategic discussions and ensure successful project execution.
Why Join Neo4j?
Neo4j is, without question, the most popular graph intelligence platform in the world. We have customers in every industry globally, and our products are a proven product/market fit. Joining our team is an opportunity to shape the future of data and analytics. Below are just a few exciting facts about Neo4j.
- Neo4j is one of the fastest\-scaling technology companies in this industry. It recently surpassed $200M in annual recurring revenue (ARR), doubling its ARR over the past three years.
- Raised the biggest funding round in database history ($325M Series F). Backed by world\-class investors like Eurazeo, GV (formerly Google Ventures), and Inovia Capital, Neo4j has raised over $600M in funding and is currently valued at over $2Bn. This puts Neo4j among the most well\-funded database companies in history.
- 84% of the Fortune 100 and 58% of the Fortune 500 use Neo4j. Examples include Boston Scientific, BT Group, Caterpillar, Cisco, Comcast, Department for Education UK, eBay, NBC News, Novo Nordisk, Worldline, and others.
- Co\-founder and CEO Emil Eifrem has built an amazing culture that prides itself on relationships, inclusiveness, innovation, and customer success.
- Countless industry awards. Massive enterprises and individual developers/data scientists love Neo4j. A strong sense of community and ecosystem is built around the platform.
- A recent Forrester Total Economic Impact™ Study cited Neo4j as delivering 417% ROI to customers.
Research shows that members of underrepresented communities are less likely to apply for jobs when they don't meet all the qualifications. If this is part of the reason you hesitate to apply, we'd encourage you to reconsider and give us the opportunity to review your application. At Neo4j, we are committed to building awareness and helping to improve these issues.
One of our central objectives is to provide an inclusive, diverse, and equitable workplace for everyone to develop their potential and have a positive, career\-defining experience. We look forward to receiving your application.
Neo4j Values:
Neo4j is a Silicon Valley company with a Swedish soul. We foster collaboration and each of us is empowered to contribute and put our innovative stamp on projects. We hire candidates who reflect the following Neo4j core values:
(we)\-\[:VALUE]\-\>(relationships)
(we)\-\[:FOCUS\_ON]\-\>(userSuccess)
(we)\-\[:THRIVE\_IN]\-\>(:Culture {type: \['Open', 'Inclusive']})
(we)\-\[:ASSUME]\-\>(:Intent {direction:'Positive'})
(we)\-\[:WELCOME]\-\>(:Discussions {nature: 'IntellectuallyHonest'})
(we)\-\[:DELIVER\_ON]\-\>(ourCommitments)
Neo4j is committed to protecting and respecting your privacy. Please read the privacy notice regarding Neo4j's recruitment process to understand how we will handle the personal data that you provide.
More information at www.neo4j.com.
©2026 Neo4j, Inc., Neo Technology®, Neo4j®, Cypher®, Neo4j Bloom™, Neo4j Graph Data Science Library™, Neo4j® Aura™, and Neo4j® AuraDB™ are registered trademarks or a trademark of Neo4j, Inc. All other marks are owned by their respective companies.
Salary Context
This $200K-$265K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1841 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Neo4j, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($232K) sits 6% above the category median. Disclosed range: $200K to $265K.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Neo4j AI Hiring
Neo4j has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in San Mateo, CA, US. Compensation range: $265K - $265K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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