Job Title

Birdeye is looking for a highly experienced Data Science Architect to design, build, and scale our AI/ML infrastructure. This high-impact, high-visibility role will be at the forefront of architecting next-generation AI solutions, optimizing ML pipelines, and deploying state-of-the-art models into production.You will work closely with data scientists, ML engineers, and software developers to bridge the gap between AI research and real-world applications. If you are passionate about MLOps, cloud-native AI architectures, and building scalable machine learning solutions, this role is for you!Key ResponsibilitiesAI/ML Architecture & Infrastructure DevelopmentDesign and build highly scalable, production-ready ML architectures for real-time and batch AI processing.Develop end-to-end ML workflows, including data ingestion, feature engineering, model training, deployment, and monitoring.Architect and optimize distributed AI computing pipelines for large-scale applications.Implement fault-tolerant, high-availability ML infrastructure for mission-critical systems.ML Model Deployment & Lifecycle ManagementDevelop and maintain CI/CD pipelines for ML models, ensuring automated deployment, version control, and rollback mechanisms.Implement model monitoring, logging, and drift detection to maintain performance in production.Automate model retraining, hyperparameter tuning, and A/B testing using scalable MLOps frameworks.Ensure secure AI deployments, enforcing role-based access control, encryption, and compliance.Data Engineering & ProcessingDesign and maintain scalable data pipelines for AI models, ensuring efficient ETL and real-time data streaming.Work with big data frameworks (Apache Spark, Hadoop, Kafka) to process petabyte-scale datasets.Implement feature stores and data versioning for reproducible AI experiments.Performance Optimization & ReliabilityContinuously optimize AI/ML infrastructure for latency, scalability, and cost-efficiency.Implement observability & monitoring using Prometheus, Grafana, ELK Stack, and other tools.Develop automated failover & self-healing mechanisms to ensure ML system reliability.Collaboration & LeadershipWork cross-functionally with data scientists, software engineers, and DevOps teams to streamline AI model deployment.Advocate MLOps best practices, enabling teams to rapidly prototype and deploy AI solutions.Mentor junior engineers and data scientists on AI engineering and operationalization.Stay ahead of AI/ML industry trends, evaluating new technologies to enhance AI/ML capabilities at Birdeye.