Jobs at Prevalent AI

Drive enterprise sales growth for an AI-driven cybersecurity platform portfolio. Identify and develop strategic opportunities, conduct customer discovery, presentations, demonstrations, and workshops, and translate cybersecurity challenges into solution opportunities. Build executive and enterprise relationships, manage complex sales cycles, forecasts, proposals, and account expansion. Collaborate with Product, Engineering, Services, Client Solutions, partners, and leadership on solution positioning, go-to-market execution, and customer growth.

The VP of Data Science will define and execute the company’s AI strategy, transform cybersecurity products into AI-first platforms, and lead development of production-grade machine learning capabilities. Responsibilities include building data science teams, establishing MLOps and AI architecture standards, deploying autonomous and LLM-driven workflows, governing responsible AI use, partnering across Product and Engineering, and representing the company through client engagements, research, publications, and industry thought leadership.

Leads client data engineering projects from requirements through production. Designs scalable Spark pipelines, diagnoses distributed job performance issues, tunes workloads to SLAs, and establishes storage and data layout strategies. Builds reusable Python frameworks, reviews code, mentors engineers, and supports containerization, object storage, CI/CD, observability, and operational documentation. The role requires deep PySpark and Spark internals expertise, large-scale time-series processing, Delta Lake or Iceberg, self-managed clusters, and hands-on coding.

Own the strategy, roadmap, growth, and lifecycle delivery of an Exposure Management application. Translate cybersecurity, vulnerability, identity, attack-path, and control data into actionable product capabilities using AI and knowledge graphs. Partner with clients, Sales, Marketing, Engineering, and Client Solutions to prioritize features, support go-to-market activities, and drive cross-sell. Lead and develop Product Owners while advancing agile, client-focused product practices.

Own and execute account-based and growth marketing campaigns targeting enterprise technology and security leaders across global markets. Manage marketing automation, intent-based prospecting, paid digital and social campaigns, funnel reporting, MQA workflows, and sales alignment. Build campaign strategy from scratch, optimize performance against pipeline goals, use AI to scale research and content operations, and support events and follow-up in a high-autonomy environment.

A DevOps Engineer at PAI designs and supports cloud-native and hybrid environments, ensuring system performance and security while automating processes and collaborating with developers.

The Data Architect will develop and govern data models within the Security Data Fabric, ensuring data integrity and flexibility while collaborating with technical teams and utilizing AI tools to enhance decision-making and model design.

Lead design, architecture, and delivery of production-grade generative AI systems (RAG, agents, vector search, LLM-backed APIs). Drive end-to-end initiatives, establish LLMOps and AI safety, deploy cloud-native AI services, mentor engineers, and produce technical documentation and runbooks for scalable, secure, production-ready AI solutions.

Lead Strategic Services to design and deliver high-value AI and data solutions for enterprise clients. Serve as senior client advisor, drive revenue growth, oversee solution design, implementation, adoption, and feedback to Product, and build scalable delivery teams and operating models.

Lead a team to design, build, and productionize AI/ML and LLM-driven security analytics. Responsibilities include model development (classical and deep learning), RAG/LLM integration, MLOps/LLMOps, monitoring, responsible AI, and mentoring engineers while shaping product strategy.

Drive enterprise cybersecurity sales for Prevalent AI platforms by identifying and expanding strategic accounts, leading consultative engagements, translating customer requirements into solutions, supporting demos/workshops, collaborating with product and services teams, and contributing to GTM and partner initiatives while maintaining pipeline accuracy and executive-level engagement.

The AI Architect will design AI-driven cybersecurity tools, lead AI initiatives, and collaborate with teams to enhance security intelligence and risk management.

Work directly with clients to elicit requirements, design and support AI-powered cybersecurity solutions (Security Data Fabric, Knowledge Graph, Exposure Management), lead workshops/demos, collaborate with product and delivery teams, and feed client insights into the product roadmap to reduce cyber risk and deliver measurable outcomes.