Machine Learning Engineer

## About the role Are you passionate about building AI-driven solutions that solve real-world problems? At Uniblox, we are modernizing the insurance industry using cutting-edge AI, NLP, LLMs and machine learning. Our platform processes structured and unstructured data in real time to deliver instant, seamless insurance experiences. As a Machine Learning Engineer, you will play a critical role in developing, deploying, and optimizing machine learning models that power our AI-driven platform. You'll collaborate with data scientists, software engineers, and product teams to bring intelligent solutions into production and continuously improve model performance. ## Responsibilities - Design, develop, and deploy machine learning models, focusing on NLP, predictive analytics, and automation. - Build and optimize scalable ML pipelines for data ingestion, feature engineering, training, and inference. - Implement, fine-tune, and monitor models in production to ensure efficiency, reliability, and accuracy. - Work closely with data scientists to translate research models into production-ready solutions. - Develop and maintain APIs to integrate ML models into real-time applications and services. - Optimize model performance and scalability through experimentation and continuous improvements. - Collaborate with software engineers to ensure seamless deployment and monitoring of ML models. - Maintain best practices in ML engineering, including version control, CI/CD pipelines, and cloud deployment. ## Requirements - 3-5 years of hands-on experience in developing and deploying machine learning models in production environments. - Proficiency in Python and experience with ML frameworks such as TensorFlow, PyTorch, or Scikit-Learn. - Strong knowledge of data structures, algorithms, and software engineering best practices. - Experience working with cloud platforms (AWS, GCP, or Azure) and deploying ML models in scalable architectures. - Hands-on experience with MLOps tools such as MLflow, Docker, Kubernetes, or SageMaker. - Solid understanding of NLP, time-series forecasting, or recommendation systems. - Experience working with large-scale data processing tools such as Spark, Dask, or Ray. - Familiarity with version control (Git), CI/CD workflows, and Agile methodologies. - Strong problem-solving skills and the ability to work in a fast-paced, collaborative startup environment. ## Nice to have - Experience with Retrieval Augmented Generation (RAG), LLMs, or Generative AI. - Knowledge of real-time streaming frameworks like Kafka. - Background in insurance or fintech industries. ## Education - BS/MS in Computer Science, Data Science, Machine Learning, or a related field.