Machine Learning Engineer, Lamini AI

• At least 5+ years of professional experience designing, training, and deploying machine learning models
• Strong computer science foundation, including data structures, algorithms, and design patterns
• Expertise in Python demonstrated by implementing multiple medium to large-scale projects
• Proven ability to implement and debug machine learning models
• Excellent communication skills and the ability to have in-depth technical discussions with both the engineering team and business people
• Familiarity with machine learning frameworks and libraries (e.g., scikit-learn, Keras, TensorFlow, PyTorch)
• Industry experience with relational databases and SQL-based tools
• BSc in Computer Science, Mathematics or similar field; Master’s or Ph.D. degree is a plus
• Self-starter and comfortable working in an early-stage environment

Desirable

• You will be responsible for end-to-end ownership of scalable Machine Learning systems — from data pipelines, to training, to analyzing performance in a production environment. Since you’ll be joining an early-stage startup at the ground level, you’ll need to be able to wear multiple hats and thrive while working in a dynamic environment
• Design and train new production-ready machine learning models. You will lead the development of new machine learning models and data pipelines. You will apply fundamental machine learning concepts to quickly iterate and debug model related issues and develop new techniques to handle unique cases with each customer
• Collect, process and analyze data. A big part of our machine learning projects is understanding and analyzing the data. You must build pipelines and processes for cleaning and organizing data as well as build tools to help analyze data. You must also understand what types of data models are struggling with and use this analysis to propose solutions
• Analyze and improve existing models. You will also be responsible for analyzing performance of our existing models and work to improve their accuracy by applying the latest published research, feature engineering and tuning of hyperparameters