Machine Learning Engineer, TBAuctions

• 7+ years of industry experience
• Master's in Computer Science, Statistics, Mathematics, or related field
• Proven experience in developing and deploying machine learning models in production environments
• Knowledge of API deployment and model serving, including containerization and orchestration with Kubernetes and Azure Kubernetes Service (AKS)
• Proven organizational and project management skills
• Expert in programming languages such as Python and Pyspark
• Strong understanding of data manipulation, statistical analysis, and machine learning algorithms (MLlib, Scikit-learn, TensorFlow/Pytorch, MLflow)
• Experience building AI agents and components such as memory, context engineering, retrieval, and orchestration, including multimodal retrieval systems/architecture
• Excellent communication skills and ability to work in a collaborative team environment

Desirable

• Develop and implement machine learning models to address business challenges
• Conduct data analysis, feature engineering, and model selection to optimize performance
• Deploy machine learning models into production systems for real-time applications
• Serve models via API deployment as scalable microservices, containerized and orchestrated with Kubernetes and Azure Kubernetes Service (AKS)
• Collaborate with software engineers to integrate models into existing infrastructure
• Build, serve, deploy, evaluate and monitor production-grade AI agents
• (LangGraph, RAG, MLFlow and FastAPI)
• Monitor model performance in production, investigate drift, and iterate to improve evaluation metrics
• Conduct A/B testing and validation to assess model impact and reliability
• Work closely with cross-functional teams to understand business requirements and translate them into technical solutions
• Communicate complex technical concepts to non-technical stakeholders effectively
• Create reproducible, versioned documentation of modeling development, experiments, and results
• Produce reports and presentations that communicate methods, findings, and recommendations