• Ensuring that machine learning models are deployed efficiently and reliably into production environments • Continuously monitoring the performance of models to detect issues like model drift and ensure they remain accurate and effective • Automating the machine learning pipeline, including tasks like data preprocessing, model training, and evaluation • Working closely with data scientists, software engineers, and IT operations to integrate machine learning models into business processes • Managing version control for models and ensuring compliance with governance policies • Identifying and implementing ways to improve the performance and scalability of ML systems • Exploring cloud tools and technologies that assist data science with implementing their usecases.

• Participate in end-to-end AI/ML projects, from research and design through deployment and ongoing maintenance, to deliver scalable production systems. • Implement ML pipelines and production-grade infrastructure that support high-volume workloads and reliable model execution. • Partner with cross-functional teams to integrate AI capabilities that align with business goals and customer needs. • Research and prototype new AI/ML algorithms and techniques to support innovation and product development. • Help establish best practices for AI/ML development, deployment, monitoring, and governance across the team. • Monitor model performance, scalability, and cost in production and contribute to continuous optimization. • Apply AI tools and techniques in day-to-day engineering work to improve productivity, support experimentation, and strengthen evaluation of model behavior, outputs, and system impact. • Advocate for responsible AI by considering fairness, transparency, data privacy, and appropriate use in model design and deployment. • Collaborate with engineering partners to move models from prototype to production using repeatable, maintainable approaches. • Document technical decisions, model behavior, and operational considerations to support shared understanding and long-term maintainability.

• Lead the design of shared platform services for agentic workflows • Define and evolve APIs, SDKs, and internal tooling for platform adoption • Design platform-level infrastructure for handling memory and context • Build and improve observability, tracing, and evaluation systems • Help design guardrails, validation layers, and policy enforcement mechanisms

• Design, construct, and manage robust data pipelines for the training, validation, and continuous retraining of Large Quantitative Models (LQMs) and agentic frameworks. • Develop, implement, and rigorously test novel ML models and algorithms, defining appropriate metrics to ensure model performance aligns with high-level product objectives. • Contribute to the efforts in cleaning, transforming, and engineering features from complex and large-scale datasets to optimize LQM performance and predictive accuracy. • Conduct deep analysis of model behavior, performance, and failure modes, tuning hyper-parameters and optimizing model architecture for efficiency, speed, and accuracy in a production context. • Collaborate closely with AI researchers, product managers, and SWEs to translate high-level business objectives into actionable ML development and deployment roadmaps. • Champion and enforce exceptional engineering standards for code quality, system efficiency, and security in a prototyping environment.