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About This Role
Description
Company Description
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The Vertex Companies, LLC (VERTEX) is a global $150M professional services firm that offers integrated forensic consulting, expert witness services, construction project advisory, and compliance and regulatory consulting to a myriad of markets and industries. Our brand purpose is to better outcomes for our clients, colleagues, and communities. Join us if you are looking for a career that offers you a chance to love what you do, continuously learn, and take pride in your work.
Job Description
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Vertex is looking for a hands\-on AI Engineer who wants to be part of a team to design and build an enterprise\-grade AI platform from scratch, integrating enterprise systems, solving real problems, and delivering measurable outcomes. You’ll work directly with the CTO to define, architect, and deploy AI solutions that transform how the business operates, from intelligent automation to agentic workflows. This is not a maintenance role, it is a ground\-up build opportunity. If you’re excited about real\-world AI use cases (not just experiments), this is your opportunity.
Core Responsibilities
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### Work Product Creation, Project Management, Coordination with Team Members
- Design and implement scalable AI solutions tailored to real business problems, not prototypes that sit on the shelf.
- Evaluate and architect build vs. buy vs. hybrid AI strategies across platforms like OpenAI, Azure OpenAI, Claude, Glean, Microsoft Copilot, Box AI, Google Vertex AI, AWS Bedrock, Databricks, Hugging Face, and more.
- Build the enterprise AI architecture, including data pipelines, orchestration layers, and model integration.
- Build RAG\-based systems, intelligent agents, automation and multi\-step AI workflows.
- Design and implement MCP server integrations to unify enterprise data sources.
- Develop orchestration layers to coordinate LLMs, APIs, tools, and workflows.
- Implement semantic search, vector indexing, and knowledge retrieval systems.
- Automate business processes using AI\-driven decisioning and workflows.
- Connect AI systems with platforms like Salesforce, Workday, ERP systems, Egnyte, Box, and other enterprise systems.
- Build robust API integrations and data pipelines to enable real\-time AI use cases.
- Ensure seamless data accessibility across structured and unstructured sources.
- Integrate enterprise systems via API and MCP to unify data sets for AI applications, ensuring seamless connectivity and data accessibility across platforms.
- Evaluate model performance, accuracy, and hallucination risks.
- Build testing frameworks, benchmarks, and validation pipelines.
- Continuously improve output based on real user feedback and usage patterns.
- Work with cloud platforms (Azure, AWS, or GCP) to deploy scalable AI solutions.
- Ensure AI systems comply with SOC 2, GDPR, HIPAA and Company security policies.
- Translate business problems into AI\-driven solutions with measurable ROI.
- Deliver improvements in efficiency, automation, and decision\-making.
- Rapidly prototype, iterate, and productionize solutions that scale.
Qualifications \& Competencies
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- Bachelor's degree and 12 years of related experience or a Master's degree and 8 years of related experience.
+ 4\+ years of experience in AI engineering
+ 8\+ years of overall experience in software engineering, architecture, data engineering and machine learning.
### Knowledge \& Skills
- Hands\-on experience with:
+ Python
+ LLMs (OpenAI, Azure OpenAI, Claude, Gemeni, etc.)
+ Vector databases
+ Cloud platforms (Azure preferred; AWS or GCP acceptable)
+ Prompt Engineering
+ RAG, MCP, Indexing and integrations
- Experience building AI solutions from ground up in self\-hosted, custom LLMs, hybrid or off he shelfs solutions.
- Preferred experience in:
+ Enterprise AI implementations
+ Document processing and knowledge systems
+ Data pipelines and ETL
+ MLOps / AI platform engineering
+ Familiarity with Salesforce, Workday, or ERP integrations
Additional Information
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At VERTEX, we invest in top talent with a highly competitive total compensation package designed to reward performance and support long\-term success. Total compensation includes a base salary and a performance\-based discretionary bonus program. Our comprehensive benefits package offers multiple healthcare and dental plan options, as well as company\-paid Life Insurance, Short\-Term Disability, and Long\-Term Disability coverage—ensuring peace of mind for you and your family.
We offer a 401(k) plan with immediate matching and full vesting, empowering employees to build financial security from day one. Additional benefits include Flexible Spending Accounts, a robust Employee Assistance Program, and a suite of exclusive perks that enhance everyday life.
At The Vertex Companies, our salary ranges are intentionally designed to support meaningful career growth over time. These ranges allow employees to develop, expand their impact, and increase their earnings as they progress within their job level. A new hire’s starting compensation is determined by their experience, geographical location, scope of the role at the time of hire, and Company affordability. Our ranges are structured to reward growth and performance, ensuring there is room for advancement and long\-term opportunity.
The salary ranges for this role are as follows:
$109,000 \- $299,000 USD annually (Geographical Tier AA \- Sample Locations: NY Metro, San Franscisco, San Jose, Seattle)
$101,000 \- $276,000 USD annually (Geographical Tier A \- Sample Locations: Irvine CA, Middlesex NJ, Tacoma WA, Boston, Alexandria)
$92,000 \- $253,000 USD annually (Geographical Tier B \- Sample Locations \- Baltimore, Chicago, Anchorage, Portland)
$84,000 \- $230,000 USD annually (Geographical Tier C \- Sample Locations \- Atlanta, Charlotte, Cincinnati, Miami)
$80,000 \- $219,000 USD annually (Geographical Tier D \- Sample Locations \- Mississippi, Mobile AL, Bowling Green KY, Tulsa)
Time away matters—so we provide a generous paid time off program, including vacation, sick time, and paid holidays (with prorated options for eligible part\-time employees).
At VERTEX, growth never stops. Our signature “Lifetime of Learning” program offers tuition reimbursement and personalized support for employees pursuing advanced education—helping you sharpen your skills and accelerate your career.
### Notice to Third Party Agencies:
Please note that The Vertex Companies employs a fully staffed recruiting team. We do not accept unsolicited resumes from recruiters or employment agencies. In the absence of a signed Recruitment Agreement, VERTEX will not consider or agree to payment of any referral compensation or recruiter fee. In the event a recruiter or agency submits a resume or candidate without a previously signed agreement, VERTEX explicitly reserves the right to pursue and hire those candidate(s) without any financial obligation to the recruiter or agency. Any unsolicited resumes, including those submitted to hiring managers, are deemed to be the property of VERTEX.
Salary Context
This $80K-$299K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $100K across 15465 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 26,159 AI roles we're tracking, AI/ML Engineer positions make up 91% of the market. At The Vertex Companies LLC, 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 $166,983 based on 13,781 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $131,300. This role's midpoint ($189K) sits 13% above the category median. Disclosed range: $80K to $299K.
Across all AI roles, the market median is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Architect ($292,900). By seniority level: Entry: $76,880; Mid: $131,300; Senior: $227,400; Director: $244,288; VP: $234,620.
The Vertex Companies LLC AI Hiring
The Vertex Companies LLC has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in US. Compensation range: $299K - $299K.
Location Context
AI roles in Austin pay a median of $212,800 across 317 tracked positions. That's 16% above the national 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 26,159 open positions tracked in our dataset. By seniority: 2,416 entry-level, 16,247 mid-level, 5,153 senior, and 2,343 leadership roles (Director, VP, C-Level). Remote roles make up 7% of the market (1,863 positions). The remaining 24,200 roles require on-site or hybrid attendance.
The market median for AI roles is $184,000. Top-quartile compensation starts at $244,000. The 90th percentile reaches $309,400. Highest-paying categories: AI Engineering Manager ($293,500 median, 28 roles); AI Architect ($292,900 median, 108 roles); AI Safety ($274,200 median, 19 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 26,159 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (23,752), AI Software Engineer (598), AI Product Manager (594). 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 (2,416) are outnumbered by mid-level (16,247) and senior (5,153) 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 2,343 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 7% of all AI roles (1,863 positions), with 24,200 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 $184,000. Top-quartile roles start at $244,000, and the 90th percentile reaches $309,400. 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 Engineering Manager roles lead at $293,500 median, while Prompt Engineer roles sit at $122,200. 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: Rag (16,749 postings), Aws (8,932 postings), Rust (7,660 postings), Python (3,815 postings), Azure (2,678 postings), Gcp (2,247 postings), Prompt Engineering (1,469 postings), Openai (1,269 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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