- Develop, train, and deploy ML models, ensuring they are optimized for production environments.
- Create and maintain automated feedback loops to enhance model accuracy and performance.
- Implement ML pipelines for continuous evaluation and refinement of models in production.
- Integrate Large Language Models (LLMs) into business applications.
- Build AI orchestration systems to manage the end-to-end lifecycle of AI models, including deployment and scaling.
- Work with Vector Databases (VectorDB) to store and query high-dimensional data for AI applications.
- Set up evaluation metrics and processes to assess model performance over time.
- Create feedback loops using real-world data to improve model reliability and accuracy.
- Develop GenAI-driven Text-to-SQL solutions to automate database queries based on natural language input.
- Optimize GenAI workflows for database interactions and information retrieval.
- Design and implement embedding and chunking strategies for scalable data processing.
- Utilize prompt engineering techniques to fine-tune the performance of AI models in production environments.
- Bachelor's or Master's degree in Computer Science, AI, Machine Learning, or a related field.
- Proven experience in building, deploying, and maintaining ML models in production environments.
- Proficiency in programming languages like Python, and frameworks such as TensorFlow, PyTorch, or similar.
- Familiarity with LLMs, VectorDB, embedding/chunking strategies, and AI orchestration tools.
- Strong understanding of model evaluation techniques and feedback loop systems.
- Hands-on experience with Text-to-SQL and prompt engineering methodologies.
- Knowledge of cloud platforms (AWS) and containerization tools (Docker, Kubernetes).
TBD