• Owns client-specific quality programs, performance metrics, and escalations while leading team development and cross-functional coordination. • Set client QA strategies (sampling design, audit methods, acceptance thresholds) and adapt to scope/volume changes. • Run root-cause analyses; drive CAPA plans with owners, timelines, and effectiveness checks. • Plan training & certification for raters/annotators and coordinators; track completion and impact. • Maintain dashboards (throughput, accuracy, productivity, cost) and convert insights into actions. • Manage client escalations; present options, trade-offs, and recovery paths. • Standardize SOPs, templates, and checklists; remove bottlenecks. • Pilot small automations (macros, templates, RPA/API handoffs) with Ops Tech; scale wins. • Coach teams on tools, workflows, and QA craft. • Ensure compliance/security across data handling and platform access.

• Design and develop machine learning models and systems for various aspects of the localization (translation) and business workflow processes • Take ownership of key projects from definition to deployment, ensuring that they meet technical requirements and maintain momentum and direction until delivery • Experiment with and evaluate classical ML and LLM-based approaches (including prompt/context engineering, retrieval-augmented generation, and agentic workflows) to identify effective solutions for business problems • Perform statistical analysis based on experimental and test results to drive measurable performance improvements • Package and deploy machine learning systems using appropriate techniques and technologies

• Designing and deploying AI-powered contact center architectures, including IVA, voice bots, chatbots, and omnichannel routing solutions. • Architecting and implementing integrations between conversational AI platforms (e.g., NICE Cognigy, WestCX Orchestrate) and enterprise systems (CRM, ERP, identity platforms). • Serving as the internal subject matter expert for conversational design, natural language understanding (NLU), and dialogue orchestration. • Leading the design and optimization of voice AI pipelines, including speech-to-text, text-to-speech, intent recognition, and real-time decisioning. • Developing and maintaining call flows, conversation flows, and automation logic, ensuring high containment and positive user experience. • Collaborating with data and analytics teams to implement AI model training, tuning, and performance monitoring, including intent accuracy and deflection rates. • Integrating AI solutions with NICE CXone contact center platform including Automated Call Summaries, Automated QA, and Real-Time Assist (Copilot). • Partnering with network and infrastructure teams to ensure low-latency, high-quality voice and API performance for real-time AI interactions. • Providing expert-level technical support for AI-driven customer engagement systems, troubleshooting issues across voice, chat, and backend integrations. • Leading the support process development and operationalization of newly deployed AI Contact Center and Virtual Agent technologies. Ensure internal teams have the skills, processes and metrics in place to support business requirements. • Leading automation and orchestration initiatives, leveraging scripting and APIs to streamline deployment, monitoring, and maintenance. • Conducting system audits to ensure compliance with security, privacy, and regulatory requirements (including call recording, data handling, and AI governance). • Staying current with advancements in generative AI, and conversational AI platforms, assessing their applicability to the enterprise. • Documenting and maintaining architecture standards, conversational design guidelines, and operational runbooks. • Supporting vendor evaluations, proofs of concept, and AI platform bake-offs to guide strategic technology decisions.

• Lead the design, architecture, testing, and deployment of production AI and machine learning solutions for the organization’s most complex and strategically important use cases. • Build the reusable data and modeling infrastructure that lets the team develop and deploy new predictive models rapidly and repeatably. • Encode the team’s existing modeling process into agentic AI workflows that can be reused across many different prediction problems. • Use agentic AI to reproduce established predictions (for example, projected medical costs), then extend the same data and process to new predictions (for example, likelihood of an emergency department visit). • Develop LLM-enabled applications, agentic workflows, and retrieval-augmented generation (RAG) systems. • Evaluate foundation models and determine the right technologies for each use case, taking into consideration relevant constraints (e.g., IT security requirements). • Build AI services and APIs that integrate into existing software products. • Optimize AI systems for quality, reliability, latency, and cost. • Define and exemplify high standards for production software engineering in AI-enabled systems, including maintainability, scalability, testing, and operational excellence. • Integrate AI capabilities into existing applications and workflows. • Build automated testing, evaluation, and deployment pipelines for AI systems. • Stay current with rapidly evolving AI technologies, research, and tooling. • Help transform research concepts into production-ready capabilities.