Role & responsibilities

- Define architectural principles and reference patterns across agent orchestration, multi-agent workflows, LLM/SLM integration, model abstraction and routing, RAG and enterprise knowledge architectures, memory/state, tool/function calling, human-in-the-loop workflows, AI evaluation, observability, security and governance.

- Drive architecture evolution of key platform capabilities including Agent Registry, Knowledge Spine, orchestration services and AI runtime components.

- Establish reusable platform patterns so current modules and client implementations do not require custom architecture each time.

- Define clear service boundaries, APIs, event patterns and integration standards across the platform.

- Design scalable and resilient patterns for multi-tenancy, session/state management, asynchronous processing, failure recovery, model fallbacks and distributed workloads.

- Work closely with existing Data Engineering and DevOps teams to ensure architecture is production-ready and deployable across CustomerInsights.AI-hosted and customer VPC environments.

- Define integration patterns with platforms such as Databricks, Snowflake, Veeva/Salesforce and enterprise data ecosystems.

- Partner with security and infrastructure teams on RBAC/RLS, SSO, auditability, tenant/data isolation, secrets management and enterprise security controls.

- Establish architecture standards around LLM independence and model portability, minimizing unnecessary dependency on any single model provider.

- Guide decisions around model selection, model routing,



commercial LLMs, open-source models and smaller language models.

- Participate in architecture reviews and provide technical direction to AI, backend, platform and product engineering teams.

- Translate product and customer requirements into scalable platform capabilities rather than one-off implementations.

- Mentor senior engineers and help raise the architecture and engineering bar across the organization.

Preferred candidate profile

- Demonstrated 0->1 experience building SaaS, AI or data products rather than only maintaining or modernizing mature platforms.

- Experience working in a startup, scale-up, founding/early engineering team, internal incubation group or entrepreneurial product environment with significant ownership over architecture and engineering decisions.

- Experience taking products from concept / prototype -> MVP -> production -> scale.

- 8-10 years of software engineering / architecture experience, including ownership of complex cloud-native or distributed systems.

- Strong hands-on engineering background with evidence of personally building, prototyping and troubleshooting production systems.

- Ability to make pragmatic architecture decisions balancing speed to market, scalability, technical debt, security, reliability and cost.

- Comfort operating in environments where requirements are evolving and architectural boundaries are still being established.

- Experience creating reusable platform capabilities rather than solving each customer or use case independently.

📌 Principal Architect - Agentic AI Platform (Gurugram)
🏢 CustomerInsights.AI
📍 Gurugram