Senior Engineer, Platform Engineering & AI Enablement

Platform Engineering & Automation

• Build and maintain reusable platform services, automation, and standard engineering patterns.
• Develop self-service capabilities that help application, data, and AI teams deliver solutions more efficiently.
• Support the evolution of internal developer platform capabilities and standardized developer workflows.
• Automate repetitive activities across cloud, data, AI, and application delivery.
• Create and maintain tools, templates, and workflows supporting architecture intake, risk assessment, and solution reviews.
• Implement platform improvements based on agreed architecture and engineering roadmaps.

Software Delivery & Developer Experience

• Build and maintain automated integration, testing, deployment, and release pipelines.
• Implement infrastructure through version-controlled code and reusable templates.
• Help standardize deployment, configuration, and release processes.
• Develop reusable workflows and guardrails that make approved engineering practices easier to follow.
• Identify opportunities to reduce manual effort and unnecessary developer friction.
• Monitor platform adoption, delivery performance, reliability, and quality.
• Maintain clear technical documentation, operational procedures, and platform guidance.

Cloud Operations, Reliability & Security

• Build and operate secure, scalable, reliable, and cost-effective cloud platform services, primarily in AWS.
• Implement monitoring, logging, alerting, dashboards, and service-health measures.
• Participate in incident response, problem investigation, and post-incident improvement activities.
• Support disaster recovery, platform patching, upgrades, and vulnerability remediation.
• Embed security and compliance controls into platform services and automated delivery workflows.
• Evaluate platform performance, reliability, security, and cost to recommend practical improvements.

Artificial Intelligence & Data Enablement

• Build reusable platform capabilities and cloud patterns for AI, machine learning, analytics, and generative AI workloads.
• Partner with data scientists, engineers, and technical teams to move solutions from development into reliable production use.
• Automate model and AI application deployment, configuration, monitoring, and lifecycle activities.
• Implement approved controls for validation, security, traceability, explainability, and regulatory compliance.
• Support AI development environments, shared services, and integration patterns.
• Evaluate emerging technologies that may improve engineering productivity, platform capabilities, or business outcomes.
• Document reusable AI deployment patterns and lessons learned for broader adoption.

Platform Support & Operational Readiness

• Ensure platform services have appropriate documentation, monitoring, support procedures, and ownership.
• Assist delivery teams with onboarding, troubleshooting, and adoption of shared platform capabilities.
• Identify recurring support requests that should be addressed through automation or self-service.
• Participate in platform health reviews and recommend corrective actions.
• Support capacity, availability, performance, and resilience planning.
• Work with service owners to transition new capabilities into sustainable operational support.

Collaboration & Technical Contribution

• Work across Architecture, Engineering, Security, Infrastructure, Product, Data, and business teams to deliver shared outcomes.
• Participate in technical design reviews and Architecture Review Board preparation.
• Provide practical technical input on platform fit, feasibility, supportability, and implementation risk.
• Share knowledge and promote consistent engineering practices across delivery teams.
• Mentor less-experienced engineers through pairing, design discussions, and technical guidance.
• Communicate technical issues, risks, dependencies, and recommendations clearly.
• Challenge inefficient practices and propose automation or platform-based solutions.