David Joseph & Company
AI Engineer — RapidCanvas
Vaga remota de AI Engineer com fit claro de localização do candidato.
Publicada4 de jul. de 2026
Países elegíveis1 país aceito
Sinal de senioridadeSenior
Modelo de trabalhoRemoto
Locais aceitos para candidatos
Estados Unidos
Resumo da vaga
AI Engineer — RapidCanvas
Requisitos e responsabilidades
Conteúdo da vaga extraído em seções para revisão mais rápida.
What You'll Own
- Design, train, and optimize ML models and LLMs to solve complex predictive and generative tasks within the RapidCanvas platform
- Architect and implement robust RAG workflows — vector database management, embedding optimization, and advanced prompt engineering
- Deploy scalable AI services using containerization and orchestration tools, ensuring high availability and low-latency inference
- Build and maintain automated data ingestion and preprocessing pipelines to transform raw enterprise data into high-quality training sets and feature stores
- Establish rigorous evaluation frameworks to measure model accuracy, drift, and computational efficiency
- Develop secure, high-performance APIs to expose AI capabilities to the frontend
Requirements
- 5+ years of professional experience moving ML models into production environments
- Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related quantitative field
- Proven experience implementing LLMs and RAG architectures using LangChain, LlamaIndex, OpenAI APIs, or similar
- Advanced Python proficiency including FastAPI or Flask for model serving
- Hands-on experience with vector databases — Pinecone, Milvus, Weaviate, or equivalent
- MLOps experience — Docker, Kubernetes, MLflow, Airflow, or similar for full ML lifecycle management
- Cloud platform experience — AWS, GCP, or Azure
- Experience with SQL/NoSQL databases and large-scale data processing
- US Citizen or Green Card holder — no visa sponsorship available
Nice to haves
- Experience with Auto-ML or No-Code/Low-Code data science platforms
- Proficiency with gradient-boosted trees (XGBoost, LightGBM), time-series forecasting, and deep learning frameworks
- Experience with automated feature engineering and hyperparameter tuning (Optuna, Ray Tune)
- Familiarity with Spark or Dask for large-scale data processing
- Master's or PhD in Computer Science, Statistics, Mathematics, or related quantitative field
Benefits
- Health, dental, and vision insurance
- Outcome-oriented flexibility — focus on impact over hours logged
Logistics
- Role is fully remote within the United States
- US Citizen or Green Card holder required — no visa sponsorship or relocation assistance available
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