• Define precise technical requirements for scalable agent architecture, including necessary reasoning frameworks, risk management systems, and knowledge retrieval interfaces required for complex decision-making. • Serve as the hands-on technical lead capable of building, testing, and deploying both interim (e.g., RAG-based systems) and highly complex agentic AI solutions. • Work alongside strategy leads to ensure that the technical prerequisites for achieving fully autonomous agentic operations are met on the twelve-month roadmap. • Oversee the technical integration and scaling of deployed AI agents within specific operational workflows (e.g., automating specific GTM tasks). • Ensure seamless deployment with rigorous validation testing, monitoring agent performance, and iterating on technical builds to improve decision accuracy and reduce latency. • Actively use AI in day-to-day work, identify where AI can change the shape of problems, and grow fluency as tools evolve.

• Drive the full lifecycle development — from design to deployment — of scalable chatbots and virtual assistants utilizing Microsoft Bot Framework, Kore.ai, or similar CAI technologies • Collaborate with cross-functional teams (product managers, designers, UX/UI specialists) to gather requirements, design conversational flows, and implement user stories • Integrate Natural Language Processing (NLP) and Natural Language Understanding (NLU) techniques to enable chatbots to understand user intent and respond accordingly • Leverage Generative AI services to enhance chatbot responses with creativity and personalization • Develop chatbots for deployment across multiple channels (web, mobile apps, messaging platforms) ensuring a seamless user experience • Conduct thorough testing and debugging to ensure high performance and accuracy of conversational AI solutions • Monitor and analyze user interactions and chatbot performance to identify areas for improvement and optimization • Stay updated on the latest advancements in Conversational AI technologies and best practices

• Design, implement, and optimize the data infrastructure that supports AI and machine learning (ML) solutions • Ensure data availability, quality, and security • Ingest and transform data for the data lake and Feature Store • Develop and support the operationalization of AI and ML models • Conduct complex analyses and monitor data quality

• Provide technical leadership to more junior members of the Applied AI Engineering team; • Identify opportunities to apply AI to drive business impact; • Perform exploratory data analysis and data visualization to understand business needs and inform LLM applications; • Develop patterns and practices to improve the scalability of LLM applications; • Optimize the Applied AI Engineering development stack (CI/CD, evaluation of libraries, etc.); • Develop AI applications using vector databases, LLMs, single-agent and multi-agent architectures; • Build validation pipelines for LLM applications, including datasets and using LLMs as a judge; • Develop small back-end and front-end components to validate proofs of concept for LLM applications; • Actively research technical blogs, academic papers and video conferences to generate improvement and optimization hypotheses for LLM applications;