Top Tech Jobs & Startup Jobs

Own Tribe’s corporate security, compliance, AI governance, and client-facing security programs. Lead SOC 2 and future certification efforts, enterprise security reviews, risk management, security controls, incident response, vendor risk, and Trust Center operations. Establish governance for AI agents, assistants, model providers, and subprocessors while partnering with engineering on architecture reviews, threat modeling, secure SDLC, data flows, and production readiness. Translate customer requirements into scalable security controls and operating processes.

Build and own the company financial model and forecast, establish metrics and planning cadence, lead analytics for resource allocation and strategy, present financial story to investors, and extend into GTM operations including capacity planning, pricing, and unit economics.

Provide end-to-end executive and personal support to the Co-Founder & CEO, manage the NY office, coordinate onboarding and vendors, plan the annual company offsite, handle company-wide admin projects, and own ad-hoc special projects and logistics to make the CEO and organization run efficiently.

Lead design of AI agent experiences—conversation and voice flows, multi-turn dialogs, escalation and error handling. Create user journeys, information architecture, wireframes, and clickable prototypes. Co-facilitate discovery workshops, ground decisions in user research, contribute to design systems, and ensure scalable, production-ready UX patterns for enterprise AI deployments.

Embed with enterprise clients to discover user needs and build AI-powered full-stack features (React/Next.js frontend, Python/Node backend). Rapidly prototype, integrate LLM APIs, harden for production, iterate with users, and contribute to internal libraries and standards.

Deploy, operate, and debug AI systems inside client infrastructure. Own production readiness, networking, observability, cost controls, IaC (Terraform/Pulumi), CI/CD and stakeholder management with client IT/DevOps to ensure scalable, governable AI deployments.

Lead client-facing AI engagements: design end-to-end AI/ML architectures, drive technical discovery, enable engineers, translate technical choices to business outcomes, and create reusable deployment patterns.

Lead technical discovery and architect end-to-end GenAI and ML systems. Build rapid prototypes, define production-grade architectures (security, observability, deployment), create reference architectures, advise stakeholders, partner with sales and delivery teams, and collaborate with OpenAI/Anthropic to validate approaches and unblock technical constraints.

Lead client-embedded product strategy and delivery for enterprise AI solutions (80% onsite). Drive solution discovery, define product vision, prioritize backlogs, prototype, and manage end-to-end implementation. Translate client work into scalable internal products, share best practices, and influence executive stakeholders to ensure value realization.

Lead client-facing AI engineering engagements: build and deploy production LLM systems using RAG, vector DBs, and agent frameworks; handle cloud deployments, CI/CD, and reliability; debug enterprise issues (auth, networking, data); embed with client teams and document solutions for reuse.