Lead Software Engineer-AI Foundation Services

Job responsibilities

• Partners with Lines of Business application teams to implement AI Foundation Services capabilities that unblock GenAI/AI use cases, supporting delivery from technical design through build, launch, and early operational support

• Builds and enhances reusable platform services, APIs, SDKs, and libraries that standardize how application teams consume model hosting, inference, and AI/ML managed services

• Translates functional and non-functional application requirements into clear technical designs, engineering tasks, and delivery milestones with support from senior engineers and architects

• Develops secure, stable, and high-quality production code, and participates in code reviews, debugging, testing, and remediation of defects across AI Foundation Services components

• Creates and maintains reusable engineering assets such as reference implementations, runbooks, test harnesses, baseline configurations, and onboarding guides to accelerate adoption across teams

• Drives team adoption of enterprise-authorized AI-assisted engineering practices to improve code quality, delivery speed, and operational outcomes, establishing consistent validation standards and promoting reuse across the team

• Applies knowledge of tools within the Software Development Life Cycle toolchain to improve automation value realization

• Designs and implements scalable software components using appropriate design patterns, cloud-native practices, and platform engineering standards

• Collaborates with cross-functional teams across product, architecture, security, infrastructure, and application development to resolve dependencies and deliver production-ready capabilities

• Contributes to technical methods, standards, documentation, and implementation patterns within AI Foundation Services, improving consistency, reliability, and reuse across delivery teams

• Communicates technical progress, risks, dependencies, and implementation options to engineering managers, product partners, and senior technical stakeholders

• Formal training or certification on software engineering concepts and 5+ years applied experience

• Strong hands‑on coding experience in one or more languages such as Python, Java, or Go, delivering production‑grade services or APIs

• Experience building shared services, reusable components, or platform capabilities consumed by multiple teams

• Experience with infrastructure‑as‑code and cloud‑native delivery practices, including Terraform, containers, Kubernetes, CI/CD pipelines, and automated deployment workflows

• Demonstrated experience leading the use of approved AI‑assisted development tools, setting expectations for validating AI outputs for correctness, performance, and security

• Strong understanding of responsible AI use in engineering workflows, including data sensitivity, secure input/output handling, and adherence to security expectations; experience coaching engineers on safe, compliant adoption

• Hands‑on experience with system design, application development, automated testing, debugging, and operational stability for production software

• Experience implementing observability, logging, metrics, alerts, Service Level Objectives, incident response, and root‑cause analysis for production services

• Working knowledge of software application development and technical processes, with depth in cloud platforms, AI/ML platforms, distributed systems, or infrastructure engineering

• Ability to break down technical requirements into executable engineering tasks, manage dependencies, and deliver milestones in partnership with product and application teams

• Strong written and verbal communication skills, explaining technical decisions, trade‑offs, issues, and risks to teams and stakeholders

• Experience supporting AI/ML or GenAI platform capabilities, including model hosting, inference services, model gateways, managed AI services, or developer‑facing AI/ML infrastructure

• Experience with GPU‑enabled platforms or AI workload optimization, inference latency, throughput, batching, capacity planning, or cost/performance tuning

• Experience building reusable…