UNKNOWN

FEED

The company offers a hybrid AI platform that integrates autonomous AI agents with human expertise, allowing businesses to build, deploy, and scale custom AI solutions significantly faster and at lower cost than traditional methods. As an AI Engineer at RapidCanvas, you will design, train, and deploy machine learning models and LLM-powered systems that power an automated machine learning platform for enterprise users.

YEAR

<h2>AI Engineer — RapidCanvas</h2><p><strong>Location:</strong> Remote (United States)<br/><strong>Compensation:</strong> $140,000 – $200,000 base<br/><strong>Visa Sponsorship:</strong> None available — US Citizen or Green Card holder required<br/><strong>Experience Level:</strong> 5+ years<br/><strong>Employment Type:</strong> Full-Time</p><h3>About RapidCanvas</h3><p>RapidCanvas is an enterprise AI company based in Austin, Texas, founded in 2021. The company offers a hybrid AI platform that integrates autonomous AI agents with human expertise, allowing businesses to build, deploy, and scale custom AI solutions significantly faster and at lower cost than traditional methods. The no-code platform supports full-lifecycle AI including data integration, predictive analytics, and workflow automation. Series A with $39.5M raised, serving manufacturing, retail, and financial services customers globally.</p><h3>About the Role</h3><p>As an AI Engineer at RapidCanvas, you will design, train, and deploy machine learning models and LLM-powered systems that power an automated machine learning platform for enterprise users. You will bridge the gap between complex data science and intuitive user experiences — owning everything from RAG pipeline architecture to production deployment and API development.</p><h3>What You'll Own</h3><ul><li>Design, train, and optimize ML models and LLMs to solve complex predictive and generative tasks within the RapidCanvas platform</li><li>Architect and implement robust RAG workflows — vector database management, embedding optimization, and advanced prompt engineering</li><li>Deploy scalable AI services using containerization and orchestration tools, ensuring high availability and low-latency inference</li><li>Build and maintain automated data ingestion and preprocessing pipelines to transform raw enterprise data into high-quality training sets and feature stores</li><li>Establish rigorous evaluation frameworks to measure model accuracy, drift, and computational efficiency</li><li>Develop secure, high-performance APIs to expose AI capabilities to the frontend</li></ul><h3>Requirements</h3><ul><li>5+ years of professional experience moving ML models into production environments</li><li>Bachelor's or Master's degree in Computer Science, Data Science, AI, or a related quantitative field</li><li>Proven experience implementing LLMs and RAG architectures using LangChain, LlamaIndex, OpenAI APIs, or similar</li><li>Advanced Python proficiency including FastAPI or Flask for model serving</li><li>Hands-on experience with vector databases — Pinecone, Milvus, Weaviate, or equivalent</li><li>MLOps experience — Docker, Kubernetes, MLflow, Airflow, or similar for full ML lifecycle management</li><li>Cloud platform experience — AWS, GCP, or Azure</li><li>Experience with SQL/NoSQL databases and large-scale data processing</li><li>US Citizen or Green Card holder — no visa sponsorship available</li></ul><h3>Nice to Have</h3><ul><li>Experience with Auto-ML or No-Code/Low-Code data science platforms</li><li>Proficiency with gradient-boosted trees (XGBoost, LightGBM), time-series forecasting, and deep learning frameworks</li><li>Experience with automated feature engineering and hyperparameter tuning (Optuna, Ray Tune)</li><li>Familiarity with Spark or Dask for large-scale data processing</li><li>Master's or PhD in Computer Science, Statistics, Mathematics, or related quantitative field</li></ul><h3>Benefits</h3><ul><li>Health, dental, and vision insurance</li><li>Outcome-oriented flexibility — focus on impact over hours logged</li></ul><h3>Interview Process</h3><ol><li>First-round team interview — technical and collaborative session</li><li>Technical assessment — practical skills evaluation or take-home assignment</li><li>Deep-dive interview — architecture, methodologies, and project experience</li><li>Cultural alignment and leadership interview with key stakeholders</li></ol><h3>Logistics</h3><ul><li>Role is <strong>fully remote</strong> within the United States</li><li>US Citizen or Green Card holder required — no visa sponsorship or relocation assistance available</li></ul><p>Shortlisted candidates will be contacted by <strong>David Joseph & Co.</strong>, the recruiting partner managing this search on behalf of RapidCanvas.</p>

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AI Engineer — RapidCanvas David Joseph & Company

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