• Build Intelligence-Driven Systems • Partner closely with the CEO, Head of Revenue, and Head of Engineering to identify high-leverage opportunities for AI-driven automation • Design systems that continuously analyze Revic data, call recordings, and customer interactions • Translate insights into automated, organization-wide workflows • Trigger GTM Actions Automatically • Use Revic data and conversations to dynamically update outbound and inbound messaging • Generate GTM content (blogs, LinkedIn posts, enablement assets) from real customer language • Surface emerging objections, value propositions, and buying signals • Turn customer conversations into living inputs for how Revic positions and sells • Extend GTM Signal Beyond GTM • Route insights from sales calls directly into product and engineering prioritization • Inform feature discovery, validation, and roadmap decisions • Identify recurring friction, gaps, and unmet customer needs • Reduce reliance on anecdotal feedback by systematizing customer signal • Operate as a Founding Builder • Act as a connective layer between GTM, product, and engineering • Identify where human interpretation slows the organization down and replace it with systems • Move from idea to prototype to production with urgency and ownership • Help define how this intelligence layer scales as Revic grows

• Lead early-phase AI systems: Design and build AI-first user experiences from the ground up, pairing rapid experimentation with strong measurement loops to evaluate performance, relevance, safety, and cost in production. • Influence product direction: Bring an engineering-driven perspective to what we build, how we build it, and which tradeoffs are worth making at early stages—charting a multi-step path that turns quick wins into durable, scalable capabilities. • Design and build scalable AI systems: Once direction is validated, evolve prototypes into reliable, maintainable, and observable production systems, balancing iteration speed with long-term platform health. • Build AI-powered product experiences: Develop AI-assisted modeling and authoring workflows to accelerate plan design, data mapping, and explanation for customers. • Deliver retrieval-augmented insights: Implement contextual help, explainers, and policy Q&A grounded in customer data and system metadata using robust retrieval-augmented generation patterns. • Own guardrails and evaluation: Design and implement evaluation and safety primitives—including offline and online evals, telemetry, human-in-the-loop workflows, safety filters, and red-teaming—to ensure trustworthy AI behavior. • Create reusable AI platform components: Build shared infrastructure for prompt orchestration, hybrid retrieval, caching, tracing, evaluation, and cost/performance controls that enable teams to ship AI features safely and efficiently. • Raise the technical bar: Contribute to architecture, coding standards, testing strategy, and mentoring; promote best practices across AI systems, platform design, and production readiness.

• Research, design, and develop innovative generative AI models and algorithms. • Implement and optimize deep learning architectures for generative tasks, focused on text generation. • Collaborate with cross-functional teams to define goals, requirements, and deliverables and implement the features. • Train and fine-tune models using large-scale datasets and advanced techniques. • Evaluate and assess model performance, making necessary adjustments to improve results. • Stay up-to-date with the latest advancements in generative AI trends and tools and contribute to the team's knowledge base. • Write clean, efficient, and maintainable code, following best practices and coding standards. • Document research findings, methodologies, and technical specifications. • Participate in code reviews and provide constructive feedback to peers. • Contribute to the development of tools and frameworks to facilitate generative AI research and development. • Interpret insights from our pre-sales, sales, customer success teams and clients to create AI-based features.

• Design, develop, and maintain backend systems in Python, integrating AI-driven functionalities and APIs • Implement prompt engineering, Retrieval-Augmented Generation (RAGs), and basic generative AI solutions into platform workflows • Collaborate on AI model deployment, fine-tuning, and production integration • Apply ML algorithms such as linear regression, logistic regression, tree-based methods, and neural networks where applicable • Ensure scalable, secure architecture leveraging AWS services (EC2, S3, Lambda, etc.) • Contribute to architectural decisions, backend infrastructure, and system optimization • Work cross-functionally with product, data science, and engineering teams to align AI initiatives with business goals • Mentor junior AI engineers as the team grows • Stay current with emerging AI technologies and assess their applicability to product needs • Business travel necessary, especially for Remote candidates