About Factify
Factify is building the truth infrastructure layer for regulated AI. Regulated enterprises are moving from AI experimentation to production deployment, but the infrastructure for trust, governance, auditability, and deterministic AI work is still being defined. Our opportunity is to help shape that category before it hardens.
What you'll do
We're looking for a VP R&D to build both the technical foundation and the engineering organization behind that platform. This is not pure people management, and it's not a chief-architect role in disguise. It's a rare combination: a deeply technical leader who can challenge the strongest engineer in the room on architecture, and who also builds an organization where talented people grow, decide independently, and own outcomes end to end.
What You'll Own
Technical direction: Define architecture and technical strategy with the CTO, Product and AI leadership. Make real tradeoffs between speed, simplicity, reliability and long-term scale. Stay deep enough to lead architecture reviews, surface hidden risks, and tell the difference between an impressive demo and a dependable production system.
An enterprise-grade agentic platform: Agent orchestration, planning and tool use · governed knowledge and policy representation · retrieval, context and memory · evaluations · human review and escalation · permissions, identity, security and auditability · reliability and observability · cost, latency and model performance · enterprise integrations.
The R&D organization: Design the structure for the company's next stage. Recruit exceptional engineers and engineering leaders at a consistently high bar. Grow managers and senior ICs who own major domains independently. Set clear expectations, feedback and performance standards - and build a leadership bench so the org doesn't depend on a handful of people.
Autonomy with accountability: Define outcomes, decision boundaries and interfaces, then push decisions as close to the problem as possible. Autonomy that produces better and faster decisions — not ambiguity, duplicated work, or diffused responsibility.
Velocity and quality together: An operating model that moves fast without normalizing instability. Better planning, testing, deployment, observability and incident learning. AI-native development as real leverage for every engineer, with clear standards for security, correctness and human judgment.
Business impact: Shape the roadmap with Product rather than receiving requirements. Join strategic customer conversations where technical leadership builds trust and unblocks adoption. Operate as a company leader, not only a function leader.
Requirements
Significant experience building complex software systems - backend, cloud, distributed systems, data platforms or enterprise architecture
Several years leading engineering organizations through managers and senior technical leaders across multiple teams
A track record of scaling an org while preserving technical quality, speed, accountability and the talent bar
Hands-on experience building or operating AI / ML / LLM / agentic systems in production
Real understanding of what makes AI agents reliable: evaluations, orchestration, context, model behavior, observability, permissions, security, failure handling
Credible contribution to architecture without becoming the approval bottleneck
Management treated as a craft: hiring, coaching, feedback, performance management, org design
Strong product and business judgment, and clear communication with engineers, executives and customers
Comfort in an early-stage environment where product, org and category are still being shaped
Nice to have
Enterprise AI or agentic platforms
Regulated or high-stakes enterprise software
Developer platforms and infrastructure
Knowledge systems, policy engines or workflow orchestration
Security, identity, authorization or compliance infrastructure