Hybrid Senior Software Engineer

at Ritchie Bros.

• Job title: Senior Software Engineer
• Job type: Full Time
• Experience level: Senior
• Salary: $108,160 - $135,220 per year
• Degree requirement: Bachelor's Degree
• Tech skills: Cloud, Django, Google Cloud Platform, Python, Scikit-Learn, Tensorflow
• Location requirements: Hybrid, Beverly Hills, United States

About the role

• Senior Software Engineer developing and maintaining backend solutions focusing on AI and ML technologies for Rouse. Leading production efforts and collaborating with data science teams.

Responsibilities

• Design, develop, debug, and deploy scalable and efficient backend and ML pipeline code.
• Collaborate with Data Science to productionize ML models, focusing on optimization (e.g., making them faster and smaller), enhancing deployability, and building robust testing frameworks.
• Perform ad hoc analysis and troubleshooting to resolve issues with deployed systems and ML models.
• Write code as part of a collaborative team, building backend features and machine learning services that play a critical role in our day-to-day operations.
• Design, implement, and maintain robust MLOPS and AIOps practices and infrastructure.
• Develop and implement AI Engineering solutions, including prompt engineering, designing Generative AI workflows, using RAG-enabled LLM calls, and implementing evaluation metrics and confidence scores from LLM outputs.
• Manage, define, and break down tasks in an agile environment.
• Mentor other team members.
• Implement with some autonomy & architect solutions in collaboration with engineering leadership.
• Own the problem and scope solutions that line up with business objectives.
• Provide a rapid response to the needs of the team.

Requirements

• Three to five years experience with Python, Django, or similar web frameworks.
• Deep experience with core ML concepts, algorithms, and libraries (scikit-learn, Tensorflow, etc.)
• Experience with techniques for model optimization and deployment (e.g., pre and post-processing, model pruning, quantization) to enhance performance and deployability.
• Familiarity with data preparation, feature engineering, and data pipeline tools.
• Familiarity with Generative AI concepts, LLMs, and prompt engineering techniques.
• Experience in building and evaluating RAG-enabled workflows and implementing confidence scoring for AI systems.
• Familiarity with Google Cloud Platform or other cloud providers for deploying scalable services and ML workload in a production environment.
• Demonstrated ability to troubleshoot, problem solve, test, and develop solutions independently.
• Ownership mindset and capable of self-managing tasks, scope, and priorities.
• Focused on providing our customers with world-class products and services.