Lead Software Engineer - Platform AI Acceleration

As Lead Software Engineer in Infrastructure Platforms Applied AI/ML, you will drive the design and delivery of modular, scalable AI-enabled systems. You will work with cross-functional teams to build reusable APIs, libraries, and reference architectures that accelerate product delivery. You will lead technical discussions, ensure secure and compliant production code, and promote enterprise AI practices across the team. You will mentor engineers and help shape firmwide standards while advancing the organization’s AI engineering capabilities. This role offers hands-on work with cutting-edge AI, focused on robust, secure, and scalable platforms within a regulated enterprise.

• Design and develop modular AI systems and interfaces aligned to platform standards, optimized for scalability and resiliency
• Collaborate with Applied AI/ML, Data Science and Cybersecurity engineers to create secure AI solutions for infrastructure platforms and consumers
• Produce secure, high-quality production code and perform code reviews and debugging
• Assess AI solution approaches from an engineering perspective to improve design, code quality, and operations
• Influence peers to adopt advanced technologies with measurable value
• Drive adoption of enterprise AI-assisted engineering practices to improve code quality, delivery speed, and operations, and promote reusable patterns
• Contribute to firmwide SDLC frameworks, tools, and practices for AI-enabled development
• Foster a team culture of opportunity, inclusion, and respect

• Hands-on system/software development experience delivering production services in secure, regulated environments
• Experience leading technical discussions across a team or program
• Significant experience in Java or Python
• Experience using AI coding assistants (GitHub Copilot, Claude Code)
• Solid understanding of building AI solutions with an LLM-backed architecture
• Strong understanding of API design, microservices, and event-driven architectures
• Experience building and operating cloud-native services on AWS, including Bedrock
• Strong DevOps practices: CI/CD, Docker/ECS/EKS, and IaC (Terraform/CloudFormation)
• Strong prompt engineering capabilities including system prompts, few-shot prompting, tool calling, and structured outputs (JSON schemas)
• Experience driving secure adoption of AI-assisted engineering tools and validating AI outputs for correctness, performance, and security

• collaboration
• mentorship
• influence
• Java
• Python
• AI coding assistants (GitHub Copilot, Claude Code)