Noida · Staff/Principal
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What you will own
The end-to-end architecture of the platform: how the integration bus, semantic layer, agent runtime, workflow engine and data stores fit together, and how that design survives multi-tenancy, 10,000 concurrent runs and a security review. You make the hard technology calls early (durable execution engine, vector store, tenancy model) and you write code for the hardest piece of each release.
Responsibilities
Own the target architecture and the architecture decision records; decide the durable workflow engine, tenancy model and data-store boundaries in the first two weeks.
Design and hands-on build the semantic layer's resolution engine (plain-language definition → systems, tools, rights) with the AI engineers.
Set the standards the team builds to: API contracts, event schemas, adapter contract, state schema for the supervisor graph, security baseline.
Run design reviews for every epic; unblock engineers on hard problems; review the code that matters.
Own non-functional requirements — scalability, isolation, latency budgets, data retention, disaster recovery — and prove them with load tests and drills before Go-Live.
Be the technical voice with pilot customers' architects and security teams.
Must have
Designed and shipped a multi-tenant SaaS or platform product used at scale; can explain the trade-offs you made and what you would change.
Deep, current knowledge of distributed systems: durable workflows or sagas, event streaming with Kafka, idempotency, back-pressure, failure modes.
Strong data architecture across relational, in-memory and vector stores; you know what belongs where and why.
Working experience with LLM-based systems in production: tool calling, agent orchestration frameworks (LangGraph or equivalent), RAG, evaluation. Not slideware — you have debugged a misbehaving agent.
Enterprise integration experience: connecting to CRMs, ERPs and legacy systems over REST, SOAP, JDBC, files; authentication and rate-limit realities.
Security architecture for B2B software: RBAC, tenant isolation, secrets, PII handling; comfortable in a pen-test readout.
Still writes production code, primarily Python; reads TypeScript.
Good to have
Temporal in production. Apache Camel or another integration framework. Kubernetes operations at scale. A published talk, paper or open-source contribution.
Responsibilities
Own the target architecture and the architecture decision records; decide the durable workflow engine, tenancy model and data-store boundaries in the first two weeks.
Design and hands-on build the semantic layer's resolution engine (plain-language definition → systems, tools, rights) with the AI engineers.
Set the standards the team builds to: API contracts, event schemas, adapter contract, state schema for the supervisor graph, security baseline.
Run design reviews for every epic; unblock engineers on hard problems; review the code that matters.
Own non-functional requirements — scalability, isolation, latency budgets, data retention, disaster recovery — and prove them with load tests and drills before Go-Live.
Be the technical voice with pilot customers' architects and security teams.
Must have
Designed and shipped a multi-tenant SaaS or platform product used at scale; can explain the trade-offs you made and what you would change.
Deep, current knowledge of distributed systems: durable workflows or sagas, event streaming with Kafka, idempotency, back-pressure, failure modes.
Strong data architecture across relational, in-memory and vector stores; you know what belongs where and why.
Working experience with LLM-based systems in production: tool calling, agent orchestration frameworks (LangGraph or equivalent), RAG, evaluation. Not slideware — you have debugged a misbehaving agent.
Enterprise integration experience: connecting to CRMs, ERPs and legacy systems over REST, SOAP, JDBC, files; authentication and rate-limit realities.
Security architecture for B2B software: RBAC, tenant isolation, secrets, PII handling; comfortable in a pen-test readout.
Still writes production code, primarily Python; reads TypeScript.
Good to have
Temporal in production. Apache Camel or another integration framework. Kubernetes operations at scale. A published talk, paper or open-source contribution.
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