• 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.

• Serve as a trusted advisor for a portfolio of high-value enterprise customers • Build executive and senior technical stakeholder relationships • Lead strategic support initiatives and account health reviews • Identify operational risks and proactively drive remediation plans • Influence customer adoption, retention, and long-term success. • Own the most complex customer issues across NICE CXone and adjacent technologies. • Lead critical escalations involving integrations, telephony, routing, analytics, APIs, and platform infrastructure. • Drive root-cause analysis and long-term resolution strategies. • Leverage AI-driven insights to scale impact across a broad portfolio. • Mentor senior engineers and emerging technical leaders. • Partner with executives and product leadership on customer-driven priorities.

• Own end-to-end ML system execution: data pipelines, training workflows, evaluation systems, inference architecture, and deployment. • Fine-tune and adapt models using state-of-the-art methods such as LoRA, QLoRA, SFT, DPO, and distillation. • Architect and operate scalable inference systems, balancing latency, cost, and reliability. • Design and maintain data systems for high-quality synthetic and real-world training data. • Implement evaluation pipelines covering performance, robustness, safety, and bias, in partnership with research leadership. • Own production deployment, including GPU optimization, memory efficiency, latency reduction, and scaling policies. • Collaborate closely with application engineering to integrate ML systems cleanly into backend, mobile, and desktop products. • Make pragmatic trade-offs and ship improvements quickly, learning from real usage. • Work under real production constraints: latency, cost, reliability, and safety