Role Overview
We are looking for a hands-on, ownership-driven CTO who will take complete responsibility for the technical architecture, team leadership, and product delivery. This is a founding leadership role — not a management overlay. You will own every technical decision from infrastructure to AI model strategy to client deployment.
What We Are Looking For
20+ years of technology experience with 8+ years in a senior technical leadership role
Proven experience architecting and delivering
multi-tenant SaaS platforms
in production
Hands-on expertise in
AI/ML system design
— production ML pipelines, model deployment, and performance management
Working knowledge of
agentic AI, LLM workflows, RAG architecture, or multi-agent systems
Strong understanding of
retail, e-commerce, or supply chain technology
— demand forecasting, inventory, pricing, or planning systems
Experience managing engineering teams of 10+ people end to end
Ability to own technical outcomes independently — architecture, delivery, and team performance
Preferred
Prior experience at retail AI or supply chain planning companies
Hands-on experience with Amazon Chronos, open-source LLMs, vector databases
Experience with SAP or Oracle ERP integration design
Background in early-stage product companies or startup environments
Demonstrated ability to optimize AI inference cost at scale
Key Expectations
Platform Architecture
Own and evolve the end-to-end technical architecture of a multi-tenant, multi-agent AI SaaS platform
Design and maintain the agent orchestration layer, policy engine, guardrails, and control tower
Define infrastructure strategy covering cloud, data layer, API gateway, scalability, and SLA management
Establish platform standards for latency, concurrency, performance, and security across all tenant deployments
Drive integration architecture with client ERP systems — SAP, Oracle, and third-party platforms — via staging layers and APIs
AI/ML Strategy
Define the AI and ML technology roadmap — model selection, algorithm priorities, and use case sequencing
Make build vs buy decisions for AI components — frontier models vs open-source, RAG vs fine-tuning
Establish model performance benchmarks, retraining cycles, and confidence threshold frameworks
Optimize AI inference cost while maintaining output quality and reliability
Design and govern human-in-the-loop escalation workflows across all platform agents
Team Leadership
Lead and scale the AIML and engineering team
Define team structure, hiring roadmap, and technical capability development plan
Establish engineering culture — delivery cadence, code quality, documentation, and testing standards
Coordinate with external technical consultants and advisors to align architectural direction
Product Delivery
Translate product requirements into technical architecture, feasibility assessments, and delivery timelines
Own MVP scoping from a technical perspective — what gets built, in what sequence, and why
Manage multi-tenant control tower — tenant creation, model assignment, policy configuration, and health monitoring
Drive technical solution design and architecture walkthroughs in client-facing conversations
