Senior Software Engineer, AI/ML (Infrastructure & Platform)

Role Senior Software Engineer, AI/ML (AI Infrastructure & Platform) Location Hybrid, NYC

About Us Wealth.com is the industry’s leading estate planning platform, empowering more than 1,000 wealth management firms to modernize how they talk about estate planning with their clients. As the only tech-led, end-to-end platform built specifically for financial institutions, Wealth.com enables firms to drive scale, efficiency, and measurable client impact. Trusted by some of the largest names in finance, Wealth.com combines proprietary AI, robust security, and deep technological and legal expertise to serve the full range of client needs, from foundational plans to the most sophisticated estate strategies. The company has been widely recognized for innovation and leadership, winning Top Estate Planning Technology and Top Estate Planning Implementation at the 2025 WealthManagement.com Industry Awards, being named the 2024 Best Technology Provider in the Trust category, and earning #1 in estate planning market share in the 2025 Kitces AdvisorTech Study.

Our team is fundamental to our standing as the leading estate planning platform. We cultivate a collaborative and supportive environment, fostering innovation and making Wealth.com a truly enjoyable workplace. Wealth.com is proud to be certified as a Great Place to Work for 2025.

The Role We are seeking a Software Engineer, AI/ML (Infrastructure & Platform) to build the foundational systems that power our next generation of AI applications.

This is a systems-focused role. You will design and build the platforms, abstractions, and infrastructure that enable teams to reliably develop, deploy, and scale AI systems — including agentic workflows, retrieval pipelines, and model integrations.

You will operate at the intersection of AI systems and distributed infrastructure, focusing on the “how” behind production AI: how models are orchestrated, how tools/skills are exposed and executed, and how systems are evaluated, monitored, and scaled in real-world environments.

Your work will directly enable product teams to move faster while ensuring our AI systems are reliable, observable, secure, and cost-efficient.

What You Will Do Build core AI infrastructure

• Design and implement platforms for LLM orchestration, tool execution, and agent workflows
• Develop shared services and abstractions used across multiple AI applications

Build AI Capability Layers (tools / Skills)

• Design and implement tools (“skills”) that agents and applications rely on, including APIs, workflows, and integrations
• Define clear interfaces for capabilities such as data retrieval, calculations, document processing, and external system actions
• Build reusable, composable abstractions that enable safe and scalable tool usage across systems
• Ensure tools are reliable, observable, and secure, especially when interacting with sensitive data

Enable agentic systems at scale

• Build infrastructure to support multi-step agents (state management, tool routing, retries, failure handling)
• Design systems where agents reason over and invoke tools/skills reliably
• Create reusable orchestration patterns between models and capabilities

Develop evaluation and observability systems

• Build frameworks for offline and online evaluation of AI systems
• Implement logging, tracing, and monitoring for model behavior and system performance

Own reliability and performance

• Design systems for high availability, fault tolerance, and graceful degradation
• Optimize for latency, throughput, and cost across AI workloads

Build data and retrieval infrastructure

• Develop scalable RAG pipelines, indexing systems, and data processing workflows
• Own infrastructure for handling large-scale structured and unstructured data

Create internal platforms and developer tooling

• Build tools, SDKs, and internal platforms that enable engineers to integrate AI capabilities quickly and safely
• Standardize best practices across teams (prompting, evaluation, deployment)

Work closely with product and AI teams

• Partner with AI Applications engineers to support production use cases
• Translate product needs into scalable infrastructure solutions

Qualifications

• A degree in Computer Science, Engineering, or a related quantitative field (or equivalent practical experience)
• Strong software engineering fundamentals, including system de…