Note: The job is a remote job and is open to candidates in USA. RapidCanvas is an enterprise AI company based in Austin, Texas, focused on delivering a hybrid AI platform that integrates autonomous AI agents with human expertise. The AI Engineer will design, train, and deploy machine learning models and systems, bridging complex data science with user-friendly experiences.
Responsibilities
- 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
Skills
- 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
- 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
Company Overview