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About This Role
Role description Sr. Neo4j \& AI Engineer
Lead II \- ML Engineering
Who We Are:
Born digital, UST transforms lives through the power of technology. We walk alongside our clients and partners, embedding innovation and agility into everything they do. We help them create transformative experiences and human\-centered solutions for a better world.
UST is a mission\-driven group of 29,000\+ practical problem solvers and creative thinkers in more than 30 countries. Our entrepreneurial teams are empowered to innovate, act nimbly, and create a lasting and sustainable impact for our clients, their customers, and the communities in which we live.
With us, you’ll create a boundless impact that transforms your career—and the lives of people across the world.
Visit us at UST.com.
You Are:
UST is searching for a Sr. Neo4j \& AI Engineer with experience working with graph databases and data modeling.
The opportunity:
- Build and maintain knowledge graph solutions that support AI, search, recommendation, and analytics use cases.
- Optimize graph storage, indexing, traversal patterns, and query performance for large\-scale deployments.
- Define and enforce data governance, security, and access controls within graph environments.
- Monitor, troubleshoot, and tune Neo4j environments for reliability, performance, and scalability.
- Azure Cloud Engineering Design, deploy, and manage graph and AI workloads on Azure.
- Implement cloud\-native solutions using Azure services such as Azure OpenAI, Azure AI Services, Azure Kubernetes Service (AKS), Azure Functions, Azure Storage, Azure Data Factory, Azure Databricks, Azure Synapse, and Azure Monitor.
- Build secure, scalable, and highly available cloud architectures following Azure best practices.
- Develop CI/CD pipelines and Infrastructure\-as\-Code (IaC) solutions using tools such as Terraform, Bicep, or ARM templates.
- Generative AI \& Claude Integration
- Design and implement AI\-powered applications leveraging Anthropic Claude and other enterprise LLM platforms.
- Build Retrieval Augmented Generation (RAG) solutions using Neo4j knowledge graphs and vector search capabilities.
- Develop prompt orchestration, agentic workflows, and graph\-enhanced reasoning solutions.
- Collaborate with AI and Data Science teams to operationalize GenAI use cases in production.
- Evaluate and optimize AI model performance, response quality, latency, and cost.
- Technical Leadership
- Provide technical leadership and mentorship to engineers across graph, AI, and cloud domains.
- Lead architecture reviews, design discussions, and implementation planning.
- Partner with stakeholders to translate business requirements into scalable technical solutions.
- Establish engineering standards, best practices, and reusable frameworks.
- Maintain comprehensive documentation of architectures, integrations, operational procedures, and design decisions
This position description identifies the responsibilities and tasks typically associated with the performance of the position. Other relevant essential functions may be required.
What you need:
- 7\+ years of software engineering experience with at least 3\+ years of hands\-on experience in Neo4j and graph database technologies.
- Strong expertise in Neo4j, graph modeling, graph theory, and Cypher query development.
- Hands\-on experience building and deploying solutions on Microsoft Azure.
- Experience designing and implementing Generative AI solutions using Anthropic Claude and/or enterprise LLM platforms.
- Proven experience building knowledge graphs, graph\-powered search, recommendation engines, or AI applications.
- Strong programming skills in Python and experience with modern backend frameworks.
- Experience integrating APIs, microservices, and distributed systems.
- Hands\-on experience with data engineering, ETL pipelines, and data integration frameworks. Experience with containerization and orchestration technologies such as Docker and Kubernetes.
- Strong understanding of software architecture, performance optimization, and security best practices.
- Preferred Qualifications:
- Experience with GraphRAG, vector databases, semantic search, and agent\-based AI architectures.
- Familiarity with Azure OpenAI, Azure AI Search, Azure Machine Learning, and related AI services.
- Experience with RDF, OWL, semantic technologies, or enterprise knowledge graph frameworks.
- Experience working with large\-scale enterprise datasets and AI\-driven applications.
- Familiarity with MLOps, LLMOps, and AI governance frameworks.
- Exposure to LangChain, LangGraph, Semantic Kernel, or similar AI orchestration frameworks.
- Neo4j, Azure, or cloud platform certifications are a plus.
Compensation can differ depending on factors including but not limited to the specific office location, role, skill set, education, and level of experience. UST provides a reasonable range of compensation for roles that may be hired in various U.S. markets as set forth below.
Role Location: Remote\-US
Compensation Range: $106,000\-$159,000
Benefits
Full\-time, regular employees accrue a minimum of 10 days of paid vacation per year, receive 6 days of paid sick leave each year (pro\-rated for new hires throughout the year), 10 paid holidays, and are eligible for paid bereavement leave and jury duty. They are eligible to participate in the Company’s 401(k) Retirement Plan with employer matching. They and their dependents residing in the US are eligible for medical, dental, and vision insurance, as well as the following Company\-paid Employee Only benefits: basic life insurance, accidental death and disability insurance, and short\- and long\-term disability benefits. Regular employees may purchase additional voluntary short\-term disability benefits, and participate in a Health Savings Account (HSA) as well as a Flexible Spending Account (FSA) for healthcare, dependent child care, and/or commuting expenses as allowable under IRS guidelines. Benefits offerings vary in Puerto Rico.
Part\-time employees receive 6 days of paid sick leave each year (pro\-rated for new hires throughout the year) and are eligible to participate in the Company’s 401(k) Retirement Plan with employer matching.
Full\-time temporary employees receive 6 days of paid sick leave each year (pro\-rated for new hires throughout the year) and are eligible to participate in the Company’s 401(k) program with employer matching. They and their dependents residing in the US are eligible for medical, dental, and vision insurance.
Part\-time temporary employees receive 6 days of paid sick leave each year (pro\-rated for new hires throughout the year).
All US employees who work in a state or locality with more generous paid sick leave benefits than specified here will receive the benefit of those sick leave laws.
What we believe:
We proudly embrace the values that have shaped UST since day one. We build our culture of Humility, Humanity, and Integrity. These values inspire us to nurture a people\-first, human centric culture that fosters diversity, prioritizes sustainable solutions, and keeps our people and clients at the forefront of all decisions.
Humility:
We will listen, learn, be empathetic and help selflessly in our interactions with everyone.
Humanity:
Through business, we will better the lives of those less fortunate than ourselves.
Integrity:
We honor our commitments and act with responsibility in all our relationships.
Equal Employment Opportunity Statement
UST is an Equal Opportunity Employer.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other applicable characteristics protected by law. We will consider qualified applicants with arrest or conviction records in accordance with state and local laws and “fair chance” ordinances.
UST reserves the right to periodically redefine your roles and responsibilities based on the requirements of the organization and/or your performance.
\#UST
\#CB
\#LI\-VB1
\#LI\-Remote
Skills
Neo4j, Generative AI, Azure Kubernetes Service, Claude
Benefits
Compensation range: $106,000\.00 to $159,000\.00 per year
About UST
UST is a global digital transformation solutions provider. For more than 20 years, UST has worked side by side with the world’s best companies to make a real impact through transformation. Powered by technology, inspired by people and led by purpose, UST partners with their clients from design to operation. With deep domain expertise and a future\-proof philosophy, UST embeds innovation and agility into their clients’ organizations. With over 30,000 employees in 30 countries, UST builds for boundless impact—touching billions of lives in the process.
Salary Context
This $106K-$159K range is in the lower quartile for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At UST, 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($132K) sits 38% below the category median. Disclosed range: $106K to $159K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
UST AI Hiring
UST has 6 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Chicago, IL, US, Austin, TX, US, Bentonville, AR, US. Compensation range: $108K - $159K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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