• 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.

• Build LLM features from ideation to production, including scaling. • Develop benchmarks to evaluate LLM performance and mitigate hallucinations. • Own the full development lifecycle, including infrastructure and front-end work around AI features. • Foster a constructive environment through code reviews, mentoring, and knowledge sharing.

• Build agent capabilities for planning, tool use, memory, and context management, and ship them into production. • Integrate agents with internal and external tools and data sources (retrieval systems, structured datasets, lab/biomed APIs, spreadsheets, search), with robust schemas and safeguards. • Develop quality and evaluation systems, including unit, regression, and scenario/benchmark tests, telemetry, and automated scoring. • Collaborate with scientists to analyze failure modes and improve performance. • Partner with the knowledge and ontology team to ensure outputs are source-traceable and compliant with provenance standards. • Implement safety measures, guardrails, and sandboxed execution for risky operations. • Optimize performance and reliability through profiling, idempotency, retries, rate limiting, and uptime management. • Instrument data pipelines for supervised fine-tuning and reinforcement learning when needed. • Contribute to the agent platform, including services, APIs, orchestration, CI/CD, and observability.