Principal Architect - Agentic AI Platform

Customerinsights.ai; Hyderabad, Gurugram District; Hybrid; INR 3,500,000 - 5,500,000; Full time

Job summary

Customerinsights.ai in Hyderabad is seeking a Senior Software Architect to define architectural principles across agent orchestration, LLM/SLM integration, memory/state, and security. You will drive evolution of platform capabilities such as Agent Registry, Knowledge Spine, and multi-tenant patterns, collaborating with Data Engineering and DevOps to ensure production readiness.

Candidates bring 8–10 years of cloud-native architecture experience, with a track record of delivering scalable SaaS or

Qualifications

• 8-10 years of software engineering / architecture experience with cloud-native or distributed systems.
• Experience building SaaS, AI or data products with architectural ownership.
• Proven ability to take products from concept to production at scale.
• Strong hands-on engineering background with prototype and troubleshooting experience.

Responsibilities

• Define architectural principles across agent orchestration, LLM/SLM integration, and security/governance.
• Drive evolution of platform capabilities like Agent Registry and Knowledge Spine.
• Establish reusable platform patterns to avoid bespoke architectures for new modules.
• Define service boundaries, APIs and integration standards across the platform.
• Mentor senior engineers and raise the architecture and engineering bar across teams.

Skills

SaaS architecture

AI/ML systems

Cloud-native

Mentoring

Job description

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 new 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.