Job responsibilities

• Architect and Design solutions to enhance the reliability and scalability of AI/ML platforms and applications to accommodate fast-growing demand
• Buildand enhance reusable platform services, APIs, SDKs, agents, skills, and libraries that standardize how application teams consume model hosting, inference, and AI/ML managed services
• Partner with AI infrastructure training, inference, and architectureteams to implement AI Foundation Services capabilities that unblock AI use cases, supporting delivery from technical design through build, launch, and early operational support
• Own and evolve non-functional requirements and build/enhance tooling for observability, resilience, security controls, infrastructure management, and cost optimization
• Establish and enforce standards and reference architectures for reliability, observability, automation, and operational readiness across services
• Partner with product and platform engineering teams to define and meet service reliability targets, including performance, availability, and recoverability
• Participate inon-call rotations, debug and resolve complex production issues; identify systemic gaps and drive durable remediation
• Mentor and guide engineers; raise the bar on engineering quality, documentation, and operational rigor

Required qualifications, capabilities, and skills

• Formal training or certification on software engineering concepts and 10+ years applied experience
• Strong hands-on coding experience in Python with experience delivering production-grade services
• Demonstrated experience leading effective use of approved AI-assisted software development tools (e.g., for coding, code review, test acceleration, troubleshooting) with the ability to set team expectations for validating AI outputs for correctness, performance, and security
• Strong understanding of responsible AI use in engineering workflows, including data sensitivity considerations, secure handling of inputs/outputs, and adherence to resiliency and security expectations; experience coaching engineers on safe, compliant adoption within delivery practices
• Hands-on practical experience with system design, automated testing, debugging, and operational stability for production software
• Experience implementing observability, logging, metrics, alerts, Service Level Objectives, incident response practices, and root-cause analysis for services in production
• Working knowledge of software application development and technical processes, with depth in one or more areas such as cloud platforms, artificial intelligence, machine learning platforms, distributed systems, or infrastructure engineering
• Ability to break down technical requirements into executable engineering tasks, manage dependencies, and deliver against milestones in partnership with product and application teams
• Strong written and verbal communication skills, with the ability to explain technical decisions, trade-offs, issues, and risks to engineering teams and stakeholders

Preferred qualifications, capabilities, and skills

• Proven skills in managing AI infrastructure on cloud platforms including deployment, scaling, monitoring, and optimizing machine learning workloads
• Experience building reusable "golden path" assets such as templates, reference implementations, SDKs, automated tests, onboarding guides, and deployment patterns
• Experience developing generative AI applications/AI agents and/or implementing AI-assisted operations with appropriate guardrails