At NiCE, we don’t limit our challenges. We challenge our limits. Always. We’re ambitious. We’re game changers. And we play to win. We set the highest standards and execute beyond them. And if you’re like us, we can offer you the ultimate career opportunity that will light a fire within you. So, what’s the role all about? A NiCE AI Forward Deployed Engineer is a highly technical full-stack engineer responsible for designing, building, and deploying AI-driven customer engagement solutions using the NiCE digital and AI portfolio. This role sits at the intersection of software engineering, AI agent development, and customer solution architecture. The engineer works directly with customers and internal stakeholders to translate complex business challenges into intelligent automation solutions across voice and digital channels. The AI Forward Deployed Engineer will design, prototype, and operationalize AI agents that integrate with enterprise systems and deliver scalable customer self-service experiences powered by intelligent virtual agents, knowledge management, and omnichannel engagement capabilities. This role requires deep technical expertise across full-stack development, AI agent architectures, APIs, integrations, and conversational automation, combined with the ability to collaborate directly with customers and business leaders. How will you make an impact? - AI Agent Design & Development - Design and build AI-driven virtual agents that automate customer service workflows across digital and voice channels. - Develop intelligent automation using the NiCE AI and Digital portfolio, including conversational AI, knowledge systems, and omnichannel engagement capabilities. - Architect agent behaviors including intent handling, workflow orchestration, and multi-system interactions. - Continuously refine and evolve agents through iterative testing, model tuning, and performance optimization. - AI Agent Lifecycle Ownership - Own the full lifecycle of AI agents from initial design through deployment and ongoing optimization. - Define agent architecture including prompting strategies, knowledge retrieval patterns, and decision logic. - Implement observability, evaluation, and feedback loops to improve agent accuracy, reliability, and business outcomes. - Monitor agent performance and evolve agents to handle increasingly complex customer interactions. - System Integration & Automation - Build integrations between AI agents and enterprise systems including CRM, commerce platforms, knowledge bases, and operational systems. - Develop APIs, services, and orchestration layers enabling agents to perform real business transactions. - Implement multi-step workflows that coordinate API calls across multiple systems of record. - Ensure secure, scalable, and reliable integrations across customer environments. - Full Stack Solution Development - Build full-stack solutions that support agent interactions including web interfaces, backend services, and integration layers. - Develop prototypes and working applications that demonstrate end-to-end AI automation scenarios. - Create reusable components and frameworks that accelerate development of new AI agent capabilities. - Maintain and enhance demo environments and development environments used for solution innovation. - Customer Solution Engineering - Partner directly with customers to understand operational challenges and identify opportunities for AI-driven automation. - Translate business requirements into technical architectures and working solutions. - Build prototypes, proof-of-value implementations, and production-ready agent capabilities. - Act as a trusted technical advisor to both business and technology stakeholders. - Product Innovation & Feedback - Collaborate with product, engineering, and sales teams to identify product gaps and innovation opportunities. - Prototype new capabilities that extend the NiCE AI platform. - Provide direct field insights that inform product roadmap and future platform capabilities. - Contribute reusable patterns, integrations, and architectures that improve future deployments. - Technical Demonstration & Enablement - Design and build advanced technical demonstrations showcasing AI-driven customer experiences. - Develop reusable demo frameworks and environments that illustrate enterprise use cases. - Support sales engineering and presales teams in communicating the value of AI-driven customer engagement solutions. - Enable internal teams with technical knowledge of AI agents, integrations, and platform capabilities. Have you got what it takes? - The AI Agent Engineer will work across several core technical domains: AI & Agent Systems - Large Language Models - Prompt engineering - Agent orchestration frameworks - Retrieval-augmented knowledge systems - Conversational AI architecture Full Stack Development - Backend services and APIs - Web and conversational interfaces - Integration layers - Data pipelines Enterprise Integrations - REST APIs and microservices - CRM and business system integrations - Knowledge systems and data stores - Identity and access management What’s in it for you? Join an ever-growing, market disrupting, global company where the teams – comprised of the best of the best – work in a fast-paced, collaborative, and creative environment! As the market leader, every day at NiCE is a chance to learn and grow, and there are endless internal career opportunities across multiple roles, disciplines, domains, and locations. If you are passionate, innovative, and excited to constantly raise the bar, you may just be our next NiCEr! Requisition ID: 10356 Reporting into: VP of Sales Role Type: Individual Contributor About NiCE NICE Ltd. (NASDAQ: NICE) software products are used by 25,000+ global businesses, including 85 of the Fortune 100 corporations, to deliver extraordinary customer experiences, fight financial crime and ensure public safety. Every day, NiCE software manages more than 120 million customer interactions and monitors 3+ billion financial transactions. Known as an innovation powerhouse that excels in AI, cloud and digital, NiCE is consistently recognized as the market leader in its domains, with over 8,500 employees across 30+ countries. NiCE is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, national origin, age, sex, marital status, ancestry, neurotype, physical or mental disability, veteran status, gender identity, sexual orientation or any other category protected by law.

• Design and build conversational AI agents for returns, claims, and customer service experiences • Own agent systems from architecture → implementation → evaluation → production operations • Build RAG / context graph retrieval pipelines that ground agent responses in real company and customer data • Design agent orchestration for multi-step workflows that interact with identity, risk, order, and loyalty systems • Create evaluation frameworks to measure task completion, accuracy, safety, and user satisfaction • Implement guardrails and safety mechanisms — content moderation, hallucination detection, graceful fallbacks • Integrate conversational experiences across web, mobile, SMS, and email channels • Make real decisions around prompt design, model selection, latency/cost/quality tradeoffs, and failure modes • Collaborate with product, design, and ML teams to build systems that are technically sound and product-aware

• Partner with stakeholders to translate complex customer challenges into robust technical designs and AI strategies • Architect, build, and deploy high-performance AI/ML platforms and tools, ensuring scalability, reliability, and security • Lead the development, evaluation, fine-tuning, and monitoring of production-grade AI/ML models (including LLMs and RAG architectures) • Design and standardize CI/CD pipelines (GitHub Actions) and Infrastructure as Code (Terraform) to ensure reproducible and automated deployments • Proactively monitor system health, lead incident response for critical AI systems, and drive architectural refactoring to remove bottlenecks • Mentor L1/L2 engineers, conduct code reviews, and foster a culture of engineering excellence and best practices • Evaluate emerging AI technologies/vendors and prototype new approaches to keep the organization at the cutting edge of the industry.

RegScale is a continuous controls monitoring (CCM) platform that helps organizations automate and scale their security, risk, and compliance programs. We are at an inflection point, transitioning from startup execution to a disciplined, enterprise ready engineering organization, and we are building the team that will take us there. AI is central to where RegScale is going, woven into how compliance programs are automated, monitored, and delivered at scale. The Role Senior AI Engineers at RegScale own the design and delivery of production AI systems end to end. You bring genuine breadth across the AI engineering stack including data pipelines, model fine tuning and evaluation, agent design and orchestration, MCP server development, and AI safeguards, and you understand how these disciplines connect. You make sound decisions across all of them, not just within a narrow specialization. Your work lives inside Platform Engineering and is consumed by product teams building GRC features and by integrators connecting RegScale to the broader compliance ecosystem. You build primitives and frameworks others build on top of, and you raise the bar for how the engineering organization thinks about and delivers AI in production. This role is for an engineer who brings the same rigor to AI systems as to any other production engineering discipline, including reliability, observability, cost management, and ongoing behavior in the real world. Key Responsibilities - Design, build, and operate AI systems in production with full ownership across reliability, performance, cost, observability, and ongoing model behavior. - Build and maintain data pipelines that ingest, clean, transform, and version the data AI systems depend on, ensuring quality and traceability from source to model. - Design and implement retrieval augmented generation pipelines, vector and graph search systems, and hybrid retrieval strategies that make compliance data accessible for AI driven features. - Fine tune, evaluate, and monitor models against real world performance criteria, with a clear understanding of how to measure what matters in a compliance domain. - Architect and build AI agent systems and orchestration layers that coordinate multi step reasoning, tool use, and decision making across complex GRC workflows. - Build and maintain MCP servers that expose RegScale platform capabilities to AI systems, enabling reliable, secure, and observable AI integrations across the platform. - Design reusable AI primitives and frameworks that product and integration teams can build on, accelerating AI feature development across the organization. - Integrate AI capabilities into CI/CD pipelines with appropriate testing, evaluation gates, and deployment strategies that maintain production quality as models and data evolve. - Partner with Platform Engineering, Core Engineering, and Compliance as Code teams to ensure AI capabilities meet enterprise reliability and security standards. - Proactively identify risks in AI system behavior, data quality, and model performance, bringing proposed mitigations before they become production incidents. Required Qualifications - 8 or more years of software engineering experience with at least 4 years focused on building and operating AI or machine learning systems in production environments. - Demonstrated track record of shipping AI features that customers depend on, with ownership across the full production lifecycle including reliability, observability, cost management, and ongoing model behavior. - Strong data engineering fundamentals, including pipeline design, data modeling, transformation, quality validation, and performance monitoring at scale. - Hands on experience with retrieval augmented generation, vector and graph databases, embedding models, and hybrid retrieval strategies. - Experience designing and building AI agent systems and orchestration frameworks, including multi step reasoning, tool use, and failure handling in production contexts. - Solid understanding of model fine tuning and evaluation, including how to define meaningful performance criteria for domain specific applications. - Strong software engineering fundamentals applied to AI systems with production grade rigor. - Strong written and verbal communication skills, able to articulate AI architecture decisions and tradeoffs to both technical and non-technical stakeholders. Preferred Qualifications - Experience building AI systems in regulated industries or compliance focused platforms where auditability, explainability, and data sensitivity shaped design decisions. - Familiarity with secure MCP server development and protocols for exposing platform capabilities to AI systems. - Background in enterprise SaaS companies where AI features had to meet enterprise reliability, security, and integration standards. - Experience with inference cost optimization, caching strategies, and model selection tradeoffs at scale. - Familiarity with GRC frameworks such as FedRAMP, NIST, or CMMC and the compliance domain more broadly. - Experience with cloud native AI infrastructure in Azure or comparable platforms including model deployment, scaling, and monitoring. - Contributions to or deep familiarity with open-source AI frameworks for orchestration, evaluation, and observability. - Experience implementing and using data lake solutions (i.e. Snowflake, Databricks, Synapse Analytics, AWS Redshift) in a production environment. RegScale is only able to hire US Citizens