Lead Software Engineer - AI/ML Engineering, GPU ML Serving

• Executes software solutions, design, development, and technical troubleshooting with ability to think beyond routine or conventional approaches to build solutions or breakdown technical problems
• Creates secure and high-quality production code and maintains algorithms that run synchronously with appropriate systems
• Leverages enterprise-authorized AI coding assist tools within the work environment to improve code quality, delivery speed, and productivity across complex deliverables (e.g., code generation/refactoring, unit test creation, documentation), while validating outputs through peer review, automated testing, and secure coding standards; contributes learnings and reusable patterns to improve broader team effectiveness.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.
• Produces architecture and design artifacts for complex applications while being accountable for ensuring design constraints are met by software code development
• Gathers, analyzes, synthesizes, and develops visualizations and reporting from large, diverse data sets in service of continuous improvement of software applications and systems
• Proactively identifies hidden problems and patterns in data and uses these insights to drive improvements to coding hygiene and system architecture

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 5+ years applied experience
• Professional software development experience, with emphasis on ML systems.
• Hands-on experience using enterprise-authorized AI-assisted software development tools within the work environment (e.g., for coding, test creation, troubleshooting, or documentation) with demonstrated ability to critically evaluate, validate, and refine AI-generated outputs for correctness, performance, and security.
• Responsible for AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; ability to guide peers on safe and effective usage within team practices.
• Experience contributing to the design and development of scalable systems and services (e.g., web services, APIs)
• Proficiency in Python, including experience with ML frameworks (e.g., TensorFlow, PyTorch, or similar)
• Experience with cloud-native technologies (e.g., Docker, Kubernetes)
• Experience with one or more public cloud platforms (AWS, GCP)
• Experience with system design fundamentals (e.g., reliability, scalability, performance tradeoffs)
• Experience working with distributed systems concepts, microservices architectures, Cassandra or equivalent NoSQL databases
• Mentorship and technical leadership experience, including setting engineering standards and elevating overall team quality

Preferred qualifications, capabilities, and skills

• Working knowledge of additional languages such as Java and/or C++
• Exposure to deploying or operating GPU workloads in Kubernetes environments
• Exposure to ML model serving frameworks (e.g., TorchServe, TensorFlow Serving, Triton Inference Server)
• Familiarity with distributed training and inference systems
• Awareness of model compression techniques and edge deployment concepts
• Familiarity with API management concepts, Domain-Driven Design (DDD) and working in Agile development environments