Machine Learning Engineer, SmartAsset

• We are seeking a highly motivated and experienced Machine Learning Engineer to join our growing team
• Proven experience as a ML Engineer with a strong focus on Python development
• Experience with cloud platforms, specifically AWS, including IAM, EKS/ECS, and other core services
• Experience operating and scaling a production application
• Experience building integrations with internal and external service providers
• Prolonged periods of sitting at a desk and working on a computer
• Must be able to communicate via phone calls and/or video conferences (mainly for concierge and sales roles)

Desirable

• The Machine Learning Engineering team designs, builds, and integrates the core platform that unlocks cutting-edge capabilities for financial advisors, including features driven by generative AI
• Our engineers are relentlessly evolving this platform, empowering groundbreaking applications and features that truly help financial professionals better connect with their clients and grow their businesses
• In this role, you will be instrumental in crafting and scaling foundational backend systems with a particular focus on our emerging GenAI initiatives
• You will work across the entire software development lifecycle, from architectural design to deployment and ongoing operations, contributing significantly to our core infrastructure and data strategies, and working hand-in-hand with cross-functional partners like LLM Engineers, Product Owners, and Data Scientists to bring our vision to life
• Design, develop, and maintain high-performance, scalable, and secure backend services, primarily using Python and frameworks like FastAPI
• Translate ambiguous business and technical requirements into concrete software designs and actionable tasks for cross-functional teams
• Work with OLTP databases, including schema design, migration strategies, and performance optimization
• Integrate SmartAsset services with internal and external APIs, including third-party platforms and existing AWS infrastructure
• Ensure secure ingress and egress of data within our private network environments
• Contribute to the design and implementation of our CI/CD pipelines to ensure continual improvements to our cycle time
• Manage and optimize AWS services, including IAM roles and policies
• Gain a deep understanding of how our Python applications integrate with our Kubernetes (EKS) and future ECS-based infrastructure
• Operate and maintain production applications at scale, ensuring high availability, performance, and reliability
• Design and implement data collection pipelines specifically for Large Language Models (LLMs)
• Contributed to shared data architecture and data governance practices to ensure the persistence of structured data suitable for analytics and model training
• Explore, integrate, and tune various LLM-related technologies, such as Retrieval Augmented Generation (RAG) systems, and addressing potential scaling challenges
• Investigate and implement agentic technologies, such as tool calling frameworks (e.g., AutoGen), to enhance LLM capabilities
• Rapidly prototype user interfaces for internal tools and data exploration, leveraging frameworks like Streamlit, Gradio, or other similar tools that facilitate quick iteration and integration without persistent websockets