Senior Software Engineer Platform Engineering, Python / ML & AI 

Role Summary: The Senior Software Engineer supports our line of business operations by building, deploying, and maintaining backend solutions, productionizing machine learning models, and enabling RAG-enhanced LLM calls using modern frameworks and technologies in accordance with industry and Rouse software engineering standards. 

Key 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 

Skills and Experience: 

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 

Expected Outcomes (first 12 months): 

Keep up with the daily needs of our operational team(s)  Build performant and scalable backend and full-stack solutions to support new and existing products and features  Partner with the Data Science (DS) team to productionize, maintain, and optimize the performance of production-grade machine learning models and services.  Successfully design and implement Generative AI features and workflows, incorporating prompt engineering and RAG into production systems.  Develop and maintain documentation for new and existing business logic in systems.  Be active in the Rouse Engineering community, performing code reviews and sharing knowledge.