Description
Chief Technology Officer
Location: Ahmedabad, India
Department: AI Lab
Experience: 20+ Years
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
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