Senior Spftware Engineer, Kiavi

• The ideal candidate should have a strong background in data science and MLOps or DevOps, with experience in designing, building, and optimizing models in non-production and production environments
• Bachelor's degree in Computer Science, Data Science, or a related field
• Experience building and testing ML models
• Experience developing and maintaining CICD Pipelines in tools like CircleCI, Jenkins, or BuildKite
• Hands-on experience with big data technologies such as Spark and Hive
• Advanced expertise in SQL and NoSQL databases
• Experience with Airflow or similar workflow orchestrators
• Familiarity with AWS, Azure, or GCP concepts
• Proven ability and aptitude for learning and applying new technologies in your work
• Strong knowledge of data structures, algorithms, and software design principles
• Excellent problem-solving skills and attention to detail
• Good written and verbal communication skills
• Ability to work in a fast-paced, collaborative environment
• Experience with online (real-time) and offline (batch) models in large production environments

Desirable

• Kiavi is looking for a skilled MLOps Engineer to join our data team
• As an MLOps Engineer, you will be responsible for designing and implementing Machine Learning platform features for our partner Data Scientists
• You will work with a variety of models ranging from online or real-time models, models which serve batched results, or large language models using Databricks, MLFlow, Python and related technologies
• Develop and maintain machine learning models, CICD solutions, and feature stores
• Collaborate with cross-functional teams, including Data Engineering, Data Science and DevOps, to ensure seamless data integration
• Participate in code reviews, ensuring code quality and best practices are followed
• Implement automated solutions to monitor and optimize data processing systems to ensure they operate efficiently and effectively
• Document and communicate data engineering solutions and processes to relevant stakeholders
• Work closely with other teams to identify opportunities for automation and process improvement