Applied Artificial Intelligence Engineer

The role sits at the intersection of ML/AI engineering and software development — it requires both building predictive/LLM models (15-2051) and shipping backend services and APIs (15-1252). 15-2051 was chosen as primary because the JD's core emphasis is on AI/ML feature design, model pipelines, and MLOps rather than general software delivery.

The title 'Applied Artificial Intelligence Engineer' carries no seniority level word, so advertised seniority is Unspecified. The 5–7 year overall requirement and 3+ years in applied ML, combined with full lifecycle ownership and strategy responsibilities, support a Senior classification.

PyTorch and TensorFlow are listed as alternatives to scikit-learn within the same 'ML frameworks' requirement; all three are captured under one skill entry.

Flask is listed as an alternative to FastAPI within the same backend-services requirement.

MLOps tools (MLflow, model registries, CI/CD for ML) appear under the preferred section; MLflow is named as the canonical example. CI/CD for ML is a practice rather than a named tool and is omitted per guidelines.

Vector databases (Pinecone, Weaviate, Chroma) are listed as preferred with 'or similar'; all three named options are captured with Pinecone as primary and the others as alternatives.

LLM provider experience, predictive modeling background, and education/healthcare sector experience appear under preferred qualifications but name no specific tools beyond what is already captured; they are omitted as they describe domain experience rather than concrete named technologies.

No compensation figures are provided in the posting.

The posting explicitly states no sponsorship is available now or in the future.

Caller marked this a fully-remote role — scored against the national candidate pool.