Machine Learning Engineer

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.

What You’ll Do

• 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.

What You’ll Bring

• 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.