Lead Backend Architect: AI-Driven Cloud-Native Systems

Veriipro

14 days+

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Node.js

JavaScript

TypeScript

Python

AWS

GCP

Kubernetes

Docker

REST APIs

gRPC

LLMs in enterprise

LangGraph

LangChain

RAG pipelines

Agentic AI

AI governance

Kafka

Terraform

CI/CD pipelines

LangGraph

LangChain

LlamaIndex

CrewAI

Semantic Kernel

Hugging Face

PyTorch

Roles & Responsibilities

Lead the architecture, design, and implementation of scalable cloud-native backend platforms for Lounge Services.
Design and develop high-performance microservices and APIs using Node.js/TypeScript on AWS/GCP.
Define architecture standards for distributed systems, event-driven solutions, messaging, and cloud-native applications.
Drive the adoption of AI capabilities by integrati ng LLMs and Agentic AI into enterprise backend services.
Design and implement reusable AI platform components including orchestration, RAG pipelines, model gateways, and AI observability.
Provide technical leadership across engineering teams, driving architecture reviews, engineering best practices, and technology decisions.
Collaborate with Product, Engineering, Security, and Enterprise Architecture teams to deliver scalable and secure solutions.
Mentor engineering teams and influence technical direction across multiple initiatives.
Evaluate emerging backend, cloud, and AI technologies and recommend enterprise adoption where appropriate.
Ensure solutions meet enterprise standards for scalability, reliability, security, performance, and operational excellence.

Must Have Technical/Functional Skills

10+ years of experience in designing and building enterprise-scale, cloud-native backend systems and distributed architectures.
Strong hands‑on expertise in Node.js, JavaScript, and TypeScript (must-have), with working knowledge of Python and Go.
Extensive experience developing microservices, REST/gRPC APIs, event‑driven architectures, and scalable backend platforms.
Strong expertise in AWS and/or GCP, Kubernetes, Docker, cloud‑native architecture, and CI/CD pipelines.
Experience with distributed messaging and streaming technologies such as Kafka, queues, and asynchronous processing.
Proven experience designing highly available, secure, scalable, and resilient backend systems.
Strong understanding of databases (SQL/NoSQL), caching, observability, logging, and performance optimization.
Mandatory experience integrating Large Language Models (LLMs) into enterprise applications and backend platforms.
Hands‑on experience with Agentic AI frameworks such as LangGraph, LangChain, LlamaIndex, CrewAI, or Semantic Kernel.
Experience building Retrieval‑Augmented Generation (RAG) pipelines, AI orchestration workflows, and LLM gateways.
Working knowledge of PyTorch, Hugging Face ecosystem, embeddings, inference, and model evaluation.
Strong understanding of AI governance, evaluation, safety, and responsible AI practices.
Excellent architecture, technical leadership, stakeholder management, and mentoring skills.