• Serve as the primary technical support partner for a portfolio of enterprise customers • Build trusted relationships with technical and operational stakeholders • Conduct account reviews, identify trends, and proactively address risks • Help customers maximize the value of their NICE investments • Partner closely with Customer Success, Services, Product, and Engineering teams • Troubleshoot advanced issues across NICE CXone and related applications • Investigate routing, telephony, analytics, integrations, APIs, and platform performance • Act as the quarterback for escalations, coordinating SMEs and Engineering teams when necessary • Drive issues to resolution while maintaining exceptional customer communication • Translate technical complexity into clear business outcomes • Utilize AI-driven tools to accelerate investigations and customer insights • Review AI-generated recommendations and apply technical judgment • Provide feedback that helps improve support automation capabilities • Use AI-powered account intelligence to identify opportunities and risks proactively • Help establish best practices for AI-augmented customer support • Share customer feedback directly with Product and AI teams • Help define how emerging AI support capabilities are used at enterprise scale • Contribute to knowledge management and support process innovation • Mentor peers and promote operational excellence

• Own the enterprise identity platform as an engineering system — architect SSO integrations, lifecycle workflows, MFA policy, and group provisioning in Okta using IaC as the source of truth; configuration drift is a bug, not a ticket • Engineer the identity lifecycle management layer — design access control models and codify provisioning/deprovisioning workflows in IaC, partnering with HR systems to eliminate manual access operations and return clean provisioning capability to the helpdesk tier • Engineer access governance across the cloud and SaaS portfolio — own role assignments, group structures, and access controls across cloud infrastructure and collaboration platforms; design and automate periodic access reviews so auditability is structural, not procedural • Harden the device-to-identity trust boundary — own device trust integrations between our identity provider and MDM platforms, ensuring device compliance signals are correctly evaluated in access policy and that the control surface is fully codified • Rationalize and consolidate legacy identity surface — lead the technical cleanup of orphaned accounts, stale SSO integrations, and guest access sprawl across the SaaS portfolio; treat this as technical debt reduction with measurable outcomes • Build the IAM measurement layer — define, instrument, and track KPIs (access review completion rates, provisioning latency, orphaned account age, ticket volume trends) that justify investment decisions and demonstrate program maturity to Security leadership • Define the identity layer of our access request platform — architect the entitlement structures and access controls exposed through our self-service access tooling; codify the configuration in IaC and own the integration contract between identity infrastructure and the employee-facing access experience • Contribute to a unified service catalog — ensure identity workflows are clearly surfaced through a single employee-facing entry point and that the handoff between self-service and engineer-owned provisioning is unambiguous • Use AI coding agents as a first-class part of the identity engineering workflow — draft and validate Terraform modules, provisioning logic, and access-policy changes with AI assistance, and own review of anything AI-generated before it reaches production identity infrastructure

• Design, develop, and optimize machine learning models for AI-powered product features involving 3D geometry, multimodal AI, and generative AI • Build and maintain scalable data pipelines and machine learning workflows for data preparation, model training, evaluation, and inference • Work with complex datasets and representations for model training, evaluation, and analysis • Design and implement evaluation methodologies, benchmarks, and experiments to measure model performance, robustness, and quality • Collaborate with researchers and engineers to transform experimental ideas into scalable, production-ready product capabilities • Build and maintain MLOps workflows supporting model versioning, deployment, monitoring, and continuous improvement • Analyze model performance, identify failure modes, and implement improvements to enhance model accuracy, reliability, and efficiency • Document and present technical designs, experimental results, and findings to collaborators and leadership

• Design, develop, and deploy end-to-end machine learning and deep learning models. • Build scalable solutions for large-volume data processing. • Develop and optimize Generative AI applications using LLMs. • Collaborate with cross-functional teams on use cases in insurance. • Ensure model performance and monitoring in production environments.