Senior AI/Machine Learning Engineer

DevIQ•Denver, CO

7h•$140,000 - $170,000•Hybrid

About The Position

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.

Requirements

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.
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.
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.
Must be a U.S. citizen or eligible to work in the U.S.

Nice To Haves

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

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.