1 day ago
Pune, IndiaStaff+
Responsibilities
- Own the enterprise AI reference architecture, target state, MVP architecture, golden paths, and reusable platform capabilities.
- Translate workflow requirements and product requirements into build-ready designs covering system boundaries, APIs, integrations, data, identity, orchestration, application layers, and non-functional requirements.
- Lead architecture and technology decisions across infrastructure, data, semantic services, AI and LLM services, agent orchestration, state and memory, deployment, and platform operations.
- Establish engineering standards for development, test, production, CI/CD, logging, monitoring, tracing, observability, evaluation, support, and operational readiness.
- Own AI operations, including model and agent lifecycle management, evaluation, drift, reliability, observability, cost management, and usage management.
- Design identity, delegated authority, least-privilege access, traceability, multi-tenancy, data isolation, and auditable agent actions across enterprise systems.
- Provide technical leadership and architecture conformance review for internal engineers, system integrators, and cloud partners.
- Define human-in-the-loop controls, escalation and handoff models, reversal and remediation processes, unit economics, model routing, caching, token budgets, and cost telemetry.
Requirements
- 10+ years of experience across software, platform, cloud, data, or enterprise architecture.
- Meaningful experience architecting production AI/ML or GenAI systems with real users, operational failure modes, and operating costs.
- Deep understanding of LLM platforms, model selection, agent orchestration, tool and API invocation, RAG, context architecture, semantic layers, state and memory, evaluation, and observability.
- Strong experience with at least one major cloud ecosystem, specifically AWS and/or GCP.
- Strong foundation in distributed systems, APIs, integration patterns, authentication and authorization, data architecture, application architecture, DevOps, CI/CD, and production operations.
- Experience designing systems spanning multiple enterprise applications and heterogeneous data sources, including systems outside the candidate’s control.
- Ability to evaluate build versus buy, reuse versus new capability, cost versus performance, and MVP versus target-state trade-offs.
- Experience converting ambiguous business requirements into technical discovery and implementable designs.
- Strong non-functional design judgment covering latency, availability, cost, failure modes, graceful degradation, and provider outages.
- Hands-on technical depth sufficient to inspect designs, APIs, data structures, prototypes, and code and credibly guide engineers.
- Strong cross-functional communication and experience aligning engineering teams, vendors, partners, and business stakeholders.
- Preferred experience with enterprise SaaS, multi-tenant platforms, customer-facing AI applications, ERP, manufacturing, supply chain, industrial software, semantic or metadata platforms, system integrators, cloud partners, interoperability standards such as MCP, or security-sensitive and regulated environments.
Benefits
- Health and well-being programs supporting work-life balance.
- Collaborative culture with growth and professional opportunity.
- Idea-sharing prioritized over level or hierarchy.
- Compensation packages based on experience and desired skill set.
- Equal opportunity workplace committed to diversity, equity, and inclusion.
Tech Stack
Categories
About QAD, Inc.
QAD builds cloud-based ERP and supply chain software for manufacturing companies, with offerings like QAD Adaptive ERP and the Redzone Connected Workforce platform, sold primarily by subscription with professional services. Its products target mid-market firms across automotive, industrial, food & beverage, life sciences, and consumer goods, embedding workflow and shop-floor capabilities. Founded in 1979 and headquartered in Miami, QAD is privately held and owned by Thoma Bravo.