Artificial Intelligence Engineer

As an AI Engineer, you will be at the forefront of building and scaling an automated machine learning platform that empowers business users to generate enterprise-grade solutions. Your daily work involves a blend of high-level architectural design and hands-on coding to bridge the gap between complex data science and intuitive user experiences. You will design, train, and optimize machine learning models and Large Language Models to solve complex predictive and generative tasks while architecting robust Retrieval-Augmented Generation workflows. This role requires you to deploy scalable AI services using containerization and orchestration tools, ensuring high availability and low-latency inference for end-user applications. You will also build automated data ingestion pipelines and establish rigorous evaluation frameworks to measure model accuracy and computational efficiency. The ideal candidate possesses at least five years of professional experience moving models from experimental notebooks into production-grade environments. You must demonstrate deep technical expertise in Large Language Models, advanced RAG architectures, and the full machine learning lifecycle through hands-on MLOps. Proficiency in Python, FastAPI, and vector databases like Pinecone or Milvus is essential for developing the secure, high-performance APIs that expose AI capabilities to the platform. We are looking for a US Citizen or Green Card holder with a strong foundation in linear algebra and statistics who can thrive in a fast-paced environment. Your success will be measured by the reliability of automated insights and the technical elegance of the scalable systems you build to support a global user base.

Responsibilities

• Model Development and Fine-tuning: Design, train, and optimize machine learning models and Large Language Models (LLMs) to solve complex predictive and generative tasks within the RapidCanvas platform.
• RAG Pipeline Engineering: Architect and implement robust Retrieval-Augmented Generation (RAG) workflows, including vector database management, embedding optimization, and advanced prompt engineering.
• Production Deployment: Deploy scalable AI services using containerization and orchestration tools, ensuring high availability and low-latency inference for end-user applications.
• Data Pipeline Integration: Build and maintain automated data ingestion and preprocessing pipelines to transform raw enterprise data into high-quality training sets and feature stores.
• Performance Benchmarking: Establish rigorous evaluation frameworks to measure model accuracy, drift, and computational efficiency, implementing continuous improvements based on quantitative metrics.
• API and Backend Integration: Develop secure, high-performance APIs to expose AI capabilities to the frontend, ensuring seamless integration with the broader platform architecture.

Qualifications

• Educational Background: Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related quantitative field with a strong foundation in linear algebra and statistics.
• Machine Learning Expertise: 3+ years of professional experience building and deploying machine learning models, specifically utilizing frameworks such as PyTorch, TensorFlow, or Scikit-learn.
• Generative AI and LLMs: Proven experience implementing Large Language Models (LLMs) and RAG architectures using tools like LangChain, LlamaIndex, or OpenAI APIs to solve complex business problems.
• Software Engineering Proficiency: Advanced programming skills in Python, including experience with FastAPI or Flask for model serving and version control using Git.
• Data Engineering and Infrastructure: Hands-on experience with SQL and NoSQL databases, vector databases (such as Pinecone, Milvus, or Weaviate), and cloud platforms like AWS, GCP, or Azure.
• Production Deployment: Demonstrated ability to manage the full ML lifecycle (MLOps) using Docker, Kubernetes, or specialized platforms to scale models in a production environment.

Ideal Candidate

The ideal candidate for the AI Engineer position at RapidCanvas possesses a deep technical foundation in machine learning operations (MLOps) and the development of automated AI platforms. This individual should demonstrate a proven track record of moving models from experimental notebooks into scalable, production-grade environments, specifically within the context of Auto-ML or No-Code/Low-Code data science platforms.

• Advanced Machine Learning Expertise: Mastery of supervised and unsupervised learning algorithms, including gradient-boosted trees (XGBoost, LightGBM), time-series forecasting (Prophet, ARIMA), and deep learning frameworks such as PyTorch or TensorFlow.
• Production-Grade Python Engineering: Exceptional proficiency in Python, with a focus on writing modular, testable, and high-performance code. This includes experience with asynchronous programming, memory management for large datasets, and performance profiling.
• Auto-ML and Feature Engineering: Significant experience building or optimizing automated feature engineering pipelines and hyperparameter tuning workflows (e.g., Optuna, Ray Tune) to ensure high model accuracy without manual intervention.
• MLOps and Infrastructure: Hands-on experience with containerization using Docker and Kubernetes, and familiarity with ML orchestration tools like Kubeflow, MLflow, or Airflow to manage the end-to-end model lifecycle.
• Data Engineering Integration: Proficiency in working with large-scale data processing frameworks like Spark or Dask, and the ability to interface with various database architectures including SQL, NoSQL, and vector databases.
• Generative AI and LLMs: Practical experience implementing Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) patterns, including prompt engineering and fine-tuning models for specific enterprise use cases.
• Cloud Architecture: Advanced knowledge of cloud service providers, specifically AWS or Azure, with a focus on SageMaker or Azure ML services for deploying and monitoring models at scale.
• API Design and Integration: Expertise in designing and maintaining robust RESTful or gRPC APIs using frameworks like FastAPI or Flask to serve model predictions to front-end applications.

Beyond technical prowess, the successful candidate must exhibit a product-centric mindset, understanding how technical decisions impact the end-user experience in a no-code environment. They should be comfortable navigating the ambiguity of a fast-paced startup, prioritizing scalability and architectural integrity while delivering iterative value.

• Problem-Solving Rigor: A methodical approach to debugging complex distributed systems and a commitment to data-driven decision-making when optimizing model performance.
• Architectural Vision: The ability to design systems that are not only functional today but are extensible enough to incorporate future advancements in AI and data science.
• Educational Background: A Master’s or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field, or equivalent professional experience in a high-growth technology environment.