Gen AI Architect

Job Overview: We are seeking a skilledData Science + Gen AI Lead/Architectto join our dynamic team. This role focuses on integrating AI models into production, optimizing machine learning workflows, and creating scalable AI-driven systems. The ideal candidate will have strong experience with Machine Learning Engineering, model evaluation techniques, feedback loop creation, and integrating advanced technologies such as Large Language Models (LLMs). The AI Engineer will also work on designing and implementing AI orchestration pipelines, with a special emphasis on prompt engineering, vector databases, and embedding strategies for efficient data handling and processing.

Key Responsibilities: Machine Learning Engineering: 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. AI Orchestration & Integration: 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. Model Evaluation & Feedback Loops: 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. Text-to-SQL & Generative AI-driven Solutions: 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. Embedding/Chunking & Prompt Engineering: 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.

Required Qualifications: Bachelors or Masters 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).