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About This Role
AI Engineer Consultant Job Description
Location: Phoenix, AZ (hybrid office schedule)
Employment Type: Full\-time
Work Authorization: Applicants must have permanent authorization to work in the United States. Plex Consulting is unable to sponsor or transfer employment visas, or provide immigration\-related employment sponsorship, now or in the future.
About the Role
We are seeking an AI Consultant to join our growing data engineering and analytics consultancy.
This is a hybrid strategist\-builder role: you will advise enterprise clients on AI strategy and solution design while also architecting and implementing the resulting solutions. You will also have the opportunity to contribute to internal initiatives, helping shape how our organization builds and productizes AI capabilities.
This role is ideal for someone who is equally comfortable translating AI opportunities into business value in a boardroom and scoping production\-grade data and AI architecture in a technical design session.
What You’ll Do
Strategy \& Advisory
- Partner with client executives and stakeholders to identify, prioritize, and scope high\-value AI and GenAI use cases aligned with business objectives.
- Develop AI/ML strategy roadmaps, maturity assessments, and business cases, including ROI, feasibility, and risk analyses, for enterprise clients.
- Advise clients on AI governance, responsible AI practices, data readiness, and the organizational change management needed to operationalize AI.
- Act as a trusted advisor in client meetings, workshops, and executive steering committees.
Solution Design
- Architect end\-to\-end AI/ML and data solutions, from data pipelines and feature engineering through model development, MLOps, and deployment.
- Translate ambiguous business problems into clear technical requirements, solution architectures, and delivery plans.
- Evaluate and recommend tools, platforms, and cloud AI services appropriate for client environments.
Design solutions involving LLMs and GenAI, including RAG, agentic workflows, fine\-tuning, and prompt engineering, where they align with client needs.
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Implementation \& Delivery
- Lead or contribute hands\-on to the buildout of AI solutions, including data pipelines, model development, integration, and deployment into production.
- Write production\-quality code and guide engineering teams on best practices.
- Manage technical delivery against scope, timeline, and budget; identify and mitigate project risks.
Ensure solutions are scalable, secure, well\-documented, and maintainable after handoff.
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Internal Initiatives
- Contribute to internal AI capability building, including reusable frameworks, accelerators, reference architectures, and internal tooling.
- Mentor junior consultants and engineers on AI/ML methods and consulting best practices.
- Support internal AI adoption initiatives, such as applying AI to delivery, knowledge management, and operations.
Contribute to practice development through point\-of\-view papers, case studies, proposal/RFP support, and pre\-sales technical input.
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Client \& Business Development
- Support pre\-sales activities, including scoping calls, technical proposals, estimating, and solution demos.
- Build long\-term, trusted relationships with client stakeholders and identify opportunities to expand engagements.
- Represent Plex at client workshops, conferences, or industry events as needed.
What We’re Looking For
Required Qualifications
- Bachelor’s degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience.
- 5\+ years of experience in data engineering, data science, ML engineering, or AI/analytics consulting, including direct client\-facing experience.
- Demonstrated experience advising on AI/ML strategy and personally delivering technical implementations; candidates should not have a strategy\-only or build\-only background.
- Strong proficiency in Python and SQL, with hands\-on experience using modern data pipelines and ML workflows.
- Practical experience with cloud platforms, such as AWS, Azure, or Google Cloud, and their AI/ML services.
- Solid understanding of the ML/AI lifecycle, including data preparation, model training and evaluation, deployment, monitoring, and retraining.
- Experience with modern GenAI/LLM technologies, including RAG architectures, vector databases, prompt engineering, and agentic frameworks.
- Excellent communication skills, with the ability to flex between technical depth with engineers and strategic framing with executives.
- Strong consulting fundamentals, including structured problem\-solving, stakeholder management, and the ability to manage ambiguity.
Preferred Qualifications
- Prior experience at a consulting firm, systems integrator, or professional services organization.
- Experience leading workstreams or small teams on client engagements.
- Familiarity with data governance, AI risk/responsible AI frameworks, and enterprise data architecture.
- Relevant certifications, such as AWS, Azure, Google Cloud, ML, or Databricks certifications.
- Industry depth in one or more verticals, such as financial services, healthcare, retail, or manufacturing.
What Success Looks Like
- Clients view you as a trusted advisor who can both set direction and deliver results.
- Engagements you lead move smoothly from strategy and discovery into successful, production\-grade implementation.
- Internal teams and junior staff grow their AI and data capabilities through your mentorship and reusable assets.
- You contribute to the firm’s AI practice growth through delivery excellence, thought leadership, and business development.
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 Plex Consulting, 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. Mid-level AI roles across all categories have a median of $200,000.
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.
Plex Consulting AI Hiring
Plex Consulting has 1 open AI role right now. They're hiring across AI/ML Engineer. Based in Phoenix, AZ, US.
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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