• Design and implement solutions leveraging Generative AI and LLM-based systems • Develop and refine prompts for optimal model performance • Analyze and validate AI outputs to ensure accuracy, fairness, and reliability • Collaborate with cross-functional teams to integrate AI into business workflows • Optimize performance and cost trade-offs in LLM deployments • Contribute to enterprise-scale GenAI initiatives, including RAG architectures and document processing use cases

• Architect and implement the backend services that power multi-agent workflows. • Design scalable workflow engines and "human-in-the-loop" systems. • Create the integration layer between modern Python-based AI frameworks and Tenable’s robust JVM-based microservices architecture. • Implement verification layers, citations, and security guardrails to ensure agents operate deterministically and safely. • Build advanced Retrieval-Augmented Generation (RAG) pipelines and "Tool Use" capabilities. • Collaborate with researchers and product leads to define the roadmap for AI orchestration.

• Design and implement workflows involving tool calling, multi-step reasoning, and structured outputs. • Build robust integrations and connectors with common internal systems such as Slack, Jira, email, calendar, CRM, and data warehouses. • Implement RAG (Retrieval-Augmented Generation) and knowledge systems for product and internal docs, ensuring citations and links to sources are included. • Build basic access control patterns, including SSO/AD group-aware retrieval and role-based response filtering. • Ship MVPs fast, iterate based on user feedback, and add necessary guardrails such as evals, logging, and retry/timeout behaviors. • Document workflows and provide simple runbooks to facilitate internal adoption.

• Understand business needs, provide actionable insights and recommendations based on analyses, and translate them into technical requirements and solution proposals. • Stay up to date with trends and advances in data science. • Apply pattern recognition techniques, including but not limited to computer vision, convolutional neural networks (CNNs), language models for natural language processing (NLP), large language models (LLMs), data analysis and predictive analytics (ML). • Ensure technical feasibility of proposed machine learning models, partnering with data scientists in designing and building solutions. • Test and validate data science solutions to ensure their effectiveness, quality, and compliance with requirements. • Engage the team and foster constructive discussions, extracting information, knowledge, and potential from the group toward a shared objective or larger outcome. • Help teams increase productivity, achieve better results, solve complex organizational problems, respond quickly to change, and strengthen their organizational culture.