Machine Learning Scientist (Engineer)

Job description
What you'll get to do...Design and implement advanced machine learning algorithms for Pricing and Generative AI projects using PyTorch and TensorFlow Develop and deploy machine learning models using Python and other state-of-the-art ML frameworksBuild APIs to deliver real-time and batch-mode predictions to customers, leveraging AWS, Docker, and cloud technologiesMonitor and optimize the performance, accuracy, and reliability of our ML solutions with logging, metrics, and dashboardsResearch and evaluate new ML techniques to enhance existing solutions and discover new opportunitiesFine-tune models to improve performance, scalability, and adaptabilityAnalyze complex datasets to inform model development and ensure accuracyStay at the forefront of advancements in machine learning and AI, incorporating new methodologies as neededCollaborate with cross-functional teams to integrate ML solutions into broader product and platform initiativesContribute to the development of standardized processes for model evaluation, validation, and production deploymentLead the exploration and adoption of innovative ML technologies, maintaining our competitive edge Your experience should include...Mid-Level: 4+ years of experience, including 2+ years in machine learning or related fields Senior-Level: 7+ years of industry experience in software engineering and 4+ years in machine learning or related fieldsProficiency in Python and machine learning algorithms, with experience in supervised/unsupervised learning, deep learning, NLP, and computer visionExperience deploying and monitoring ML models in production using AWS services such as SageMakerHands-on experience building APIs for ML model serving with frameworks like Flask, FastAPI, or DjangoStrong understanding of software engineering best practices (version control, CI/CD, code reviews)Ability to work both independently and collaboratively in cross-functional teamsExperience writing unit tests and documentation for ML code You might also have...Experience working with large-scale, complex datasets Applied machine learning experience in industries like e-commerce, finance, or healthcareFamiliarity with ML lifecycle management tools such as MLflow, Kubeflow, or AirflowExpertise with common ML libraries like TensorFlow, PyTorch, Keras, and Scikit-learnHands-on experience with large language models (LLMs) and prompt engineeringProficiency in multiple programming languagesFamiliarity with containerization technologies like Docker and orchestration tools like Airflow