DevIQ
Senior AI/Machine Learning Engineer
Remote Consulting role with clear candidate location fit.
PostedJul 4, 2026
Eligible countries1 accepted country
Seniority signalSenior
Work settingRemote
Accepted candidate locations
USA
Role overview
Senior AI/Machine Learning Engineer
Requirements and responsibilities
Readable role content extracted into sections for faster review.
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.
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
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
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.
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