AI Engineer (Tech Co-founder, Equity-Share)

What we need:

We’re looking for an AI engineer with a founder’s mindset — fast, obsessed, and relentless. This is a mission-critical role to build and deliver a high-impact MVP on a tight timeline. You must think big, move fast, and lead like it’s your own. We're not hiring talent. We're aligning with a future co-founder — someone ready to build groundbreaking AI products and shape the future from day zero.

We’re looking for an entrepreneur, not just an engineer. Someone with the vision to build a tech empire of recurring revenue-generating SaaS products, human-like AI agents.

About TimeAI:

Time AI is a U.S.-based startup on a mission to build the future of intelligent automation. We're creating the world’s first AI Agent Factory — a scalable platform that produces, trains, and deploys specialized AI agents for businesses across industries.

Our focus is speed, precision, and impact. From healthcare to e-commerce, our agents automate critical workflows, optimize operations, and unlock new levels of productivity.

We're starting with a time-sensitive MVP for a major Dubai healthcare client (with 5 medical centers), and scaling toward a marketplace for AI agents — where companies can plug in powerful, ready-to-work AI talent in seconds.

At Time AI, we don’t just save time.

We build the future with it.

Project: Advanced Multi-Agent AI Systems for Business Automation:

We're not building simple chatbots. We're developing production-grade, multi-agent AI systems that autonomously coordinate to solve complex business problems. Our flagship product, CareBot, is the first of multiple planned agent systems that will expand into real estate, e-commerce, and other sectors.

Technical Ecperience:

1. Expert-Level Multi-Agent System Architecture

- Proven experience: architecting and deploying multi-agent systems that have handled real-world production workloads

- System design expertise: creating resilient agent architectures that can operate continuously in business-critical environments

- Advanced orchestration patterns: complex agent collaboration workflows with proper state management

- Production implementation: agent specialization strategies where multiple agents with distinct expertise collaborate on complex tasks

2. Production AI Engineering Expertise:

- High-performance inference optimization: techniques for reducing latency in LLM-based agent systems

- Scalable vector database implementation: proper indexing strategies for efficient semantic search in production

- Agent memory persistence: solutions that maintain context across sessions while optimizing for cost and performance

- End-to-end testing methodologies: multi-agent systems including simulation environments and evaluation frameworks

- Cost optimization strategies: managing token usage in production LLM applications

3. Advanced LLM & Agent Framework Mastery:

- Expert-level prompt engineering: creating robust, deterministic agent behaviors that work consistently in production

- Custom agent framework development: beyond off-the-shelf solutions to meet specific business requirements

- Function calling optimization: efficient tool use by agents in production environments

- Agent planning systems: implementation with fallback mechanisms and error handling

- Advanced RAG techniques: hybrid search, reranking, and context compression strategies

4. Production System Integration:

- Secure API integration patterns: connecting agent systems with external business systems

- Real-time communication protocols: implementation for agent-to-agent and agent-to-human interactions

- Authentication and authorization: implementation for enterprise-grade agent systems

- Monitoring and observability: setup for production AI systems including token usage, latency, and accuracy metrics

- CI/CD pipelines: continuous deployment of agent systems with proper testing gates

5. Technical Stack Expertise (Production Level)

- NodeJS/NestJS expertise: building enterprise-grade microservices with proper error handling and logging

- FastAPI/Python mastery: high-performance AI service development with async processing

- PostgreSQL with pgvector: implementation including optimized index strategies and query patterns

- Docker container orchestration: reliable scaling of multi-agent systems in production

- Redis or similar: implementing efficient caching and state management in distributed agent systems

Professional Experience:

• Entrepreneurial mindset with a bias for action and ownership.
• Ability to think strategically and execute tactically under extreme time pressure.
• Proven experience leading tech initiatives from concept to production.
• Strong communication skills — able to align tech vision with business goals.
• Decision-making under uncertainty and high-stakes environments.
• Resilience and persistence in solving complex technical and operational challenges.
• Collaborative leadership — able to build, motivate, and manage high-performance teams.
• Obsession with quality, scalability, and future-proofing technical architectures.
• Client-oriented thinking — able to balance speed of delivery with real-world reliability.
• Growth mindset — committed to continuous learning and self-improvement.
• High emotional intelligence — able to handle conflict, feedback, and co-founder dynamics.
• Visionary thinking — sees beyond the MVP to scalable products and long-term market dominance.

Compensation Model:

""IMPORTANT: NO SALARY. LOOKING FOR STRATEGIC, INVOLVED PARTNER""

This position is based purely on a future revenue-sharing model:

- You will receive a percentage of future revenue generated by CareBot and other AI-powered solutions you develop

- Added as a Stripe collaborator with full visibility into finances and direct access to your share

- Formal recognition as a Co-Founder with appropriate legal agreements

Project Timeline & Status:

- "URGENT NEED": We have an immediate deadline to deliver a working MVP within a week

- Our first major client (a Dubai-based owner of five medical centers) is finalizing their contract this week

- We're targeting 100 clients by the end of this year across multiple business sectors