• Design and build agentic systems — multi-step agents that plan, call tools, retrieve context, and take action with appropriate human-in-the-loop checkpoints • Build MCP servers and clients to securely expose client data, internal tools, and APIs to LLMs in a standardized, auditable way • Ship LLM-powered applications: copilots, document intelligence, search, summarization, and workflow automation • Design and maintain RAG pipelines — chunking, embeddings, vector stores, retrieval, reranking, and grounding • Integrate model APIs (OpenAI, Anthropic, Bedrock, Azure OpenAI, open-weight models) and pick the right model for the job based on quality, latency, and cost • Develop evals and observability for agents and AI features so we know what's working in production and what's regressing • Apply prompt engineering, structured outputs, function/tool calling, and guardrails to make agent behavior predictable • Write production Python backends and APIs that expose AI capabilities to web and mobile clients • Collaborate with engineers, designers, and product folks to scope what AI should (and shouldn't) do in a given product • Help shape responsible AI practices for federal use — privacy, security, auditability, and human oversight

• Design and deploy scalable, cloud-based architectures for AI-driven marketing applications. • Lead AI research and development efforts from early exploration through production delivery. • Partner with cross-functional stakeholders to translate business needs into testable AI solutions. • Build and optimize data pipelines, retrieval systems (RAG), vector databases, and multi-agent workflows. • Develop and maintain backend services and RESTful APIs using Java, Node.js, or Python. • Own end-to-end solution design, including prototyping, testing, training, and support processes. • Evaluate and prioritize AI initiatives based on feasibility, scalability, and business impact. • Communicate technical concepts and strategic recommendations clearly to senior stakeholders. • Document system architecture and best practices to ensure long-term scalability and maintainability. • Mentor team members and guide adoption of emerging AI technologies.

• Develop novel reinforcement learning algorithms to solve complex, real-world problems. • Integrate Large Language Models (LLMs) to enhance agent reasoning, threat intelligence, or human-machine interactions. • Implement and extend Message Control Protocol (MCP) to enable coordinated behavior across modular AI components or multi-agent systems. • Prototype new methods and help transition successful prototypes into deployed solutions. • Collaborate with cross-functional teams (Data Science, Engineering, Product) to integrate and deploy RL models in production. • Conduct thorough evaluations of model performance using appropriate experimental design and statistical analysis. • Write clean, maintainable and well-documented code. • Participate in code reviews and ensure the code quality of your team. • Troubleshoot and debug complex software issues.

• Own the complete AI platform portfolio across three categories: AI-enabled UX, AI agents (MCP/agentic automation), and AI in data processing. • Drive embedded AI features from internal alpha through production — managing rollout criteria, prompt iteration, success rate thresholds, and the decision framework for when each feature is ready for customer availability. • Define and maintain Redox's AI investment framework: KPIs, rollout criteria, cost guidelines, and the research agenda for what we still need to learn (monitoring, agentic loops, RAG, re-prompting). • Manage beta relationships for agentic use cases across multiple customer segments. • Lead Redox's agentic product strategy — starting with the MCP Server as the current foundation, expanding into native agent support for high-value customer workflows throughout 2026, with a clear 2027 vision & roadmap. • Equip sales and CS to position Redox's AI platform story accurately. • Scope and drive overall platform features required to unlock new use case, channel partnerships, & commercial success in the Enterprise customer base. • Ensure complex infrastructure work (VPN configurations, multi-tenant patterns, integration partner requirements) is scoped clearly and investments are made with commercial need. • Partner with GTM & Customer Success to enable vendor customer growth (both new business and expansion). • Contribute to use case development, customer segmentation, & field enablement to close high-value Enterprise vendor deals.