• Design and build AI solutions (with a strong focus on LLMs and modern ML) for real-world business problems. • Own the end-to-end lifecycle of AI features: problem definition, data understanding, modeling, evaluation, deployment, monitoring, and iteration. • Develop and optimize LLM-based applications (e.g. RAG, agents, chatbots, document understanding). • Participate in the design and management of data pipelines for efficient data collection, preprocessing, and analytics. • Develop and deploy AI solutions into the company’s cloud infrastructure, ensuring scalability, security, and performance. • Work closely with cross-functional teams to integrate AI capabilities into products, services, and internal tools. • Identify and prioritize areas where AI and machine learning can add measurable value to business processes, products, and operations. • Educate and guide stakeholders on the potential applications of AI, fostering a culture of innovation.

• Build and maintain **AI agents** capable of reasoning, planning, and executing tasks • Design and implement **Retrieval-Augmented Generation (RAG)** pipelines • Apply effective **data chunking, embedding, and retrieval strategies** • Integrate **vector databases** into agent workflows • Use and extend **AI agent frameworks** (LangChain, LlamaIndex, AutoGen, CrewAI, etc.) • Connect agents to APIs, internal systems, and external tools • Optimize prompts, agent logic, and memory for reliability and performance • Deploy, monitor, and iterate on AI systems in production • Collaborate closely with product and engineering teams

• Consult on and enable successful execution of Data Science AI/ML and Generative AI solutions • Educate customers and stakeholders on the value of Red Hat's Data Science AI/ML offerings • Lead and enable on hyper-competitive sales situations • Serve as a geo-level conduit of GTM for Red Hat AI • Work closely with Red Hat AI product teams for continuous improvement • Build lasting relationships with cloud providers for joint GTM • Interface with regional sales leadership for account planning

• Design, implement, and optimize LLM-powered systems (e.g., RAG, chat agents, summarizers, knowledge graph integration). • Build and manage data indexing and retrieval pipelines using LlamaIndex, LangChain, or similar frameworks. • Implement and maintain vector databases (e.g., Pinecone, Neo4j, Weaviate, Chroma, or Azure Cognitive Search). • Integrate open-source and proprietary LLMs (e.g., GPT, Claude, Llama) into the CoreStory Platform. • Develop and refine AI-driven features — including generative insights, automated summarization, and narrative analytics. • Collaborate with DevOps and backend teams to deploy scalable AI services within CoreStory’s cloud infrastructure. • Continuously benchmark model performance, latency, and cost, identifying opportunities for optimization. • Stay current with advancements in AI — from model architectures to emerging frameworks — and propose innovative applications aligned with CoreStory’s mission. • Contribute to internal documentation, experimentation frameworks, and evaluation methodologies.