Senior AIMachine Learning Engineer

Were 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. Youll 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 DevIQs 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
• Bachelors or Masters degree in Computer Science Machine Learning or equivalent practical experience

Additional Information :

Est. Salary Range (Colorado Only): $140000-$170000*

*Disclaimer: In accordance with Colorados 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

Remote Work :

Yes

Employment Type :

Full-time