Role & responsibilities Design, develop, and implement Java-based applications using Spring Boot and microservices architecture. Collaborate with cross-functional teams to gather requirements and translate them into robust software solutions. Write clean, efficient, and well-documented code that adheres to industry best practices. Develop and maintain RESTful APIs for seamless communication between microservices. Build and deploy microservices on cloud platforms (e.g., AWS, Azure, Google Cloud). Implement unit and integration tests to ensure the quality and reliability of code. Troubleshoot and resolve issues in a timely manner. Continuously improve software performance, scalability, and security. Stay updated with the latest trends and technologies in Java, Spring Boot, and microservices. Experience with cloud platforms such as AWS, Azure, or Google Cloud. Experience with message brokers like Kafka or RabbitMQ. Familiarity with containerization technologies like Docker and Kubernetes. Strong understanding of design patterns and object-oriented programming principles.
Role & responsibilities 5 years of hands-on industry data science experience, using typical machine learning and data science tools including pandas, mlflow, scikit-learn, gensim, nltk, and TensorFlow/PyTorch Experience building production-grade machine learning deployments on AWS, Azure, or GCP including drift monitoring Experience with the latest techniques in natural language processing including vector databases, fine-tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI Graduate degree in a quantitative discipline (Computer Science, Engineering, Statistics, Operations Research) or equivalent practical experience Experience communicating and teaching technical concepts to non-technical and technical audiences alike Passion for collaboration, life-long learning, and driving value through ML [Preferred] Experience working with Apache Spark to process large-scale distributed datasets [Preferred] Experience working with the Databricks platform
Role & responsibilities 4+ years of experience in data engineering, data architecture, data platforms & analytics At least 3+ years of experience with Databricks, PySpark, Python, and SQL. Consulting / customer-facing experience, working with external clients across a variety of industry markets Comfortable writing code in both Python and SQL Proficiency in SQL and experience with data warehousing solutions Working knowledge of two or more common Cloud ecosystems (AWS, Azure, GCP) with expertise in at least one Strong understanding of data modeling, ETL processes, and data architecture principles. Deep experience with distributed computing with Apache Spark and knowledge of Spark runtime internals Familiarity with CI/CD for production deployments - GitHub, Azure DevOps, Azure Pipelines Working knowledge of MLOps methodologies Design and deployment of performant end-to-end data architectures Experience with technical project delivery - managing scope and timelines Documentation and white-boarding skills Experience working with clients and managing conflicts Build skills in technical areas that support the deployment and integration of Databricks-based solutions to complete customer projects Good to have Databricks Certifications