• Own the end-to-end lifecycle of AI-driven features—from prototyping and experimentation to production deployment and customer delivery. • Design, build, and evolve our semantic data layer to enable scalable, high-quality AI and analytics use cases. • Collaborate with engineering, product, and data teams to translate ambiguous requirements into robust technical solutions. • Diagnose, triage, and resolve production issues quickly, ensuring reliability and continuous improvement of deployed systems.

• Work alongside software developers and architects, UX / UI visual designers and architects, and product developers • Develop, implement, and configure web content management solutions using AEM (Adobe CQ) and technologies such as Java, HTML/DHTML, XML, JavaScript, and web services • Discover, design, and build highly interactive interfaces and content that keep users engaged and build positive brand equity at every possible point in the user experience • Work with Adobe Experience Manager (CQ5 and AEM6) as well as other CMS • Ability to work closely with back end developers to produce solutions and smooth integrations • Setup and configuration of AEM Author and Publish instances and ensuring all code works in both environments • Delivering product and apps that are based on pre-existing wireframes, designs, or other client requirements

• Design, develop, and maintain software solutions using Node.js, Nest.js, and React.js, with a focus on AI-powered features across the platform. • Build and evolve AI-based capabilities such as: LLM-powered features for intelligent automation, summarization, classification, and decision support across CCaaS workflows. • Agentic workflows using tools like MCP, function calling, and multi-step reasoning pipelines. • Real-time AI integrations with voice and digital channels (transcription, sentiment, intent detection, etc.). • Embedding and retrieval systems for RAG-based features and knowledge-augmented experiences. • Implement and drive event-driven architectures using Kafka to support async AI processing pipelines. • Lead small to medium technical projects end-to-end — from scoping and technical design to delivery and retrospective. • Navigate tech and product tradeoffs — work closely with Product and Design to challenge scope, propose alternatives, and land on pragmatic solutions without losing sight of outcomes. • Work effectively with contractors — onboard, align, and maintain quality standards across mixed teams. • Mentor engineers and raise the technical bar through code reviews, design discussions, and knowledge sharing. • Translate ambiguous business needs into clear technical direction — and keep stakeholders aligned along the way. • Keep technical documentation sharp and actively contribute to engineering standards.

• Lead the Design of Business-Critical Systems: Own end-to-end technical design and delivery for the services that power namespace scalability, reliability, and the customer data products built on top of them. • Bridge Instrumentation and Product Value: Partner with analytics and product engineers to ensure what gets collected actually powers meaningful customer insights; translate business questions into durable event schemas that outlast the feature that introduced them. • Architect Data Pipelines for Scale and Correctness: Define and evolve our data pipeline architecture for high-throughput usage and operational data; make principled tradeoffs across data freshness, query cost, tenant isolation, and fault-tolerance. • Drive Data Quality as a First-Class Concern: Establish schema contracts, pipeline-level anomaly detection, and alerting for silent failures — holding the line on data correctness before bad numbers reach customers. • Raise the Engineering Bar: Set quality standards through code and design reviews; introduce patterns and tooling that improve maintainability, observability, and incident response across the team. • Mentor and Multiply Impact: Grow the engineers around you, unblock execution, and foster a culture of ownership, learning, and pragmatic excellence. • Improve Developer and Customer Experience: Identify opportunities to streamline customer flows via automation, better tooling, and targeted platform improvements that reduce friction and support load.