Senior AI/Machine Learning Engineer

Role 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

• 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.
• 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.

Requirements

• 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.

Benefits

• 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.