Interested in this AI/ML Engineer role at DevIQ?
Apply Now →Skills & Technologies
About This Role
Company Description
DevIQ specializes in building modern cloud and data solutions – and we believe in the power of software and technology to improve lives. Join us to partner with passionate mid\-market companies focused on reducing energy costs, curing disease, improving education, building smart cities, and more. From true innovation and synergetic cloud \& technology partnerships to competitive full\-time benefits and a strong team culture, DevIQ is a great place to work.
At DevIQ, you’ll:
- Build your career with a supportive, inclusive team that appreciates people, creates value, embraces growth, and “owns the problem” as a team.
- Enjoy opportunities to learn, exposure to new industries, and building end\-to\-end solutions through meaningful work on active client projects.
- Work remotely and/or from our modern studio in downtown Denver.
- Bring your unique perspective and experience to multi\-disciplinary teams.
- Collaborate on and contribute to transformative digital experiences that touch millions of lives, watching your work make an impact.
Please note that you must be a U.S. citizen or eligible to work in the U.S. to be considered for this role, and third\-party candidates will not be accepted.
Job Description
We’re looking for a hands\-on Senior AI/Machine Learning Engineer to design, build, and deploy AI and machine learning solutions that solve real business problems for our clients. This is a consulting role that blends hands\-on engineering, applied AI/ML expertise, and client\-facing advisory work. You’ll partner directly with client stakeholders to understand their goals, translate ambiguous problems into well\-scoped solutions, and see your work through from prototype to production. Success in this role depends as much on communication, empathy, and professionalism as it does on technical depth.
Key Responsibilities:
- Own ML solutions end to end — framing the business problem, exploring data, training and evaluating models, and iterating based on rigorous error analysis — through to production deployment and monitoring
- Apply generative AI and LLMs where they fit the problem, selecting appropriate techniques and adapting as the field evolves
- Establish MLOps best practices: CI/CD for models, experiment tracking, model and drift monitoring, and responsible\-AI practices
- Translate ambiguous business problems into well\-scoped solutions, setting clear expectations on feasibility, timelines, and trade\-offs
- Serve as a trusted technical advisor — presenting demos and recommendations, and explaining models, their limitations, and uncertainty clearly to audiences from engineers to executives
- Mentor teammates and collaborate across multi\-disciplinary teams of engineers, data scientists, and designers
- Adapt quickly to new industries, tools, and client environments while staying current with the evolving AI landscape
- Operate as a flexible consulting engineer within DevIQ’s delivery model, contributing beyond AI/ML when project needs and team availability require it, including adjacent work such as discovery, data exploration, data engineering, application development, DevOps, solution documentation, technical analysis, internal tooling, or other client\-supporting utility tasks.
Qualifications Required:
Machine learning depth
- 4\+ years building, training, and deploying ML models in production — owning the modeling work, not just integrating model APIs.
- Strong modeling fundamentals: framing a problem as a learning task, feature engineering, model selection, and reasoning about bias/variance, regularization, and overfitting.
- Rigorous evaluation discipline: sound train/val/test methodology, avoiding data leakage, choosing metrics that fit the business goal, and error analysis to diagnose why a model underperforms.
- Deep learning fundamentals — architectures, loss functions, training dynamics — enough to build and debug models in PyTorch or TensorFlow, not just call them.
- Solid math/stats foundation (linear algebra, probability, statistics) and the judgment to know when ML is the right tool versus a simpler approach.
Applied AI and engineering:
- Hands\-on LLM/generative\-AI delivery — RAG, embeddings, fine\-tuning, and major model APIs (e.g., Anthropic, OpenAI, Bedrock) — with judgment to choose between prompting, retrieval, and fine\-tuning.
- Strong Python and the modern ML stack (PyTorch or TensorFlow, scikit\-learn), plus solid SQL.
- Experience deploying and monitoring ML workloads on at least one major cloud (AWS, Azure, or GCP), including versioning, drift monitoring, and retraining.
Consulting and communication:
- Client\-facing or consulting experience, able to explain technical trade\-offs — including model limitations and uncertainty — to non\-technical stakeholders
- Self\-directed and comfortable with ambiguity across multiple engagements
- Willingness and ability to work beyond a narrowly defined AI/ML role, contributing to adjacent engineering, data, discovery, DevOps, consulting, and utility activities as needed in a project\-based consulting environment.
Preferred:
- Experience with Databricks, lakehouse architectures, or large\-scale data engineering workflows
- Experience supporting pre\-sales efforts (solution design, scoping, and estimating)
- Depth in one or more ML domains — e.g., NLP, computer vision, time\-series forecasting, or recommender systems
- Research or open\-source signal in ML — publications, patents, notable contributions, or competition results
- Bachelor's or Master's degree in Computer Science, Machine Learning, or equivalent practical experience
Additional Information
Est. Salary Range (Colorado Only): $140,000\-$170,000\*
- Disclaimer: In accordance with Colorado’s Equal Pay for Equal Work Act, effective January 1, 2021, a good faith hourly or base salary range must be posted for all positions where the work may be performed in the state of Colorado. Therefore, this good faith salary range will only apply where this described position will be performed in the state, and should not be considered the compensation range in other locations or for other positions.
DevIQ Benefits Include:
- Competitive financial compensation and utilization bonus plans
- Medical, Dental, Vision Insurance
- 401k, With 4% Matching
- Paid Time Off
- Health Savings Account (HSA)/Flexible Spending Account (FSA)
- Short\-Term/Long\-Term Disability Insurance
- Business funded Life Insurance Plan
- Dynamic yet relaxed work atmosphere
- Wide Variety of Growth Opportunities
Salary Context
This $140K-$170K range is below the median for AI/ML Engineer roles in our dataset (median: $180K across 1937 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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At DevIQ, 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($155K) sits 14% below the category median. Disclosed range: $140K to $170K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
DevIQ AI Hiring
DevIQ has 2 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Based in Denver, CO, US. Compensation range: $170K - $175K.
Location Context
AI roles in Denver pay a median of $184,000 across 159 tracked positions. That's 8% below 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.