Senior Director of Engineering, DataRobot

• Extensive experience in building and managing machine learning platforms, with deep expertise in ML Ops functions such as model registry, model deployment, tracking, and monitoring
• Proficiency in programming languages such as Python, Java, or similar, with experience in machine learning frameworks like TensorFlow, PyTorch, or similar
• Strong experience in working with cloud environments (AWS, Azure, GCP) and container orchestration technologies like Kubernetes and Docker
• Bachelor's degree in Computer Science, Software Engineering, Data Science, or a related field. Master's or PhD preferred
• 15+ years of experience in machine learning, data science, software engineering or related fields and 7+ years of experience in a leadership role, scaling, managing and mentoring technical teams

Desirable

• The Senior Director of Engineering for Machine Learning Operations (ML Ops) will lead and oversee multiple software and ML teams, responsible for developing and scaling our robust ML Ops platform
• This individual will guide the strategy and execution of key ML Ops functions, such as model deployment, registry, tracking, governance, monitoring, and observability
• They will ensure that our end-to-end AI/ML platform continues to excel in the industry with its capabilities for efficient deployment, management, and monitoring of models across all stages of their lifecycle
• These capabilities span both predictive and generative offerings at DataRobot
• Provide architectural and technical guidance for building ML Ops infrastructure, including model deployment pipelines, model registries, monitoring systems, and governance frameworks
• Ensure adherence to best practices for software development, automation, and machine learning engineering
• Oversee the scalability, reliability, and security of the ML platform in production environments
• Lead and support different modes of model deployment, including real-time, batch, streaming, and edge deployments, ensuring models operate efficiently across diverse environments (e.g., cloud, on-premises, IoT devices)
• Design flexible deployment strategies to accommodate varying use cases such as low-latency predictions, offline model inference, and multi-model management
• Ensure the platform supports both batch and real-time machine learning workflows at scale
• Lead the design and implementation of machine learning lifecycle management systems to handle model deployment, versioning, rollback mechanisms, and real-time monitoring
• Ensure the platform supports both batch and real-time machine learning workflows at scale
• Drive the automation of key processes in model deployment and operations to increase speed and reduce manual intervention, as well as drive business, product and technical changes within the organization
• Collaborate with product management, data science, and infrastructure teams to align on business needs and ensure smooth integration of machine learning models into production systems. Act as the liaison between engineering teams to ensure the successful delivery of ML Ops solutions
• Develop governance policies and frameworks around model management, ensuring models are properly tracked, documented, and auditable for regulatory compliance
• Ensure production environments are monitored effectively, with appropriate observability, alerting, and response protocols in place for handling issues
• Lead efforts to improve system performance and reduce downtime, maintaining high availability and reliability standards for ML-driven products
• Champion continuous improvements to the ML Ops platform, focusing on reducing operational overhead and improving the efficiency of machine learning workflows