Salary / Hourly Rate: $140,000 - $200,000 per year
Benefits: N/A
Role Information
Role Overview: N/A
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.
Minimum Requirements: US Citizen or Green Card holder,5 years of professional experience moving machine learning models into production environments,Professional experience implementing Large Language Models and Retrieval-Augmented Generation architectures,Proficiency in Python and FastAPI for model serving,Hands-on experience with vector databases such as Pinecone, Milvus, or Weaviate,Experience with MLOps tools including Docker and Kubernetes for model scaling,Bachelor or Master degree in Computer Science, Data Science, or a quantitative field,Experience with cloud platforms specifically AWS, GCP, or Azure
Screening Questions: N/A
Company Information
About Company: N/A
Culture: N/A
Additional Information
Interview Process: First-Round Team Interview: A technical and collaborative session with your potential teammates to discuss your background, explore your problem-solving approach, and align on the core requirements of the role., Technical Assessment: A practical skills evaluation or take-home assignment designed to showcase your proficiency through a real-world scenario relevant to our specific technology stack., Deep-Dive Interview: A comprehensive session with our subject matter experts to explore your methodologies, architectural thinking, and previous project experiences in granular detail., Cultural Alignment & Leadership: A final strategic conversation with key stakeholders to ensure mutual values fit and to discuss your potential impact on the team’s long-term growth and success.
Day to day: Model Architecture and Development: You will design, implement, and fine-tune state-of-the-art machine learning models, including LLMs, time-series forecasting, and classification algorithms, ensuring they are optimized for the RapidCanvas automated workflow., AutoML Pipeline Engineering: You will build and maintain robust automated pipelines that handle feature engineering, model selection, and hyperparameter optimization, reducing the time-to-value for end-users from weeks to hours., Scalable Inference Systems: You will develop and deploy high-performance inference services using Python, FastAPI, and Docker, ensuring that models can handle large-scale datasets with minimal latency in a cloud-native environment., Algorithm Optimization: You will analyze model performance metrics and implement advanced techniques such as quantization, pruning, or prompt engineering to improve accuracy and computational efficiency., Data Engineering Integration: You will collaborate with data engineers to build seamless integrations with various data sources (Snowflake, S3, SQL), ensuring that the underlying data structures are optimized for machine learning consumption., Technical Documentation and Prototyping: You will create detailed technical specifications for new AI features and build rapid prototypes to validate the feasibility of emerging technologies before they are integrated into the core platform., Code Quality and Peer Review: You will participate in rigorous code reviews and contribute to the internal ML library, ensuring that all production code meets high standards for maintainability, scalability, and security., Performance Monitoring: You will implement monitoring and observability tools to track model drift and system health in production, proactively identifying and resolving bottlenecks in the AI execution engine.
Team: N/A
Growth: N/A
Ideal Candidate Profile: 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., 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.
