About the Role
Job Description:
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.Requirements:
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.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