Lead Backend Architect: AI-Driven Cloud-Native Systems

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/Type Script 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.

• 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 Type Script (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/No
  
  SQL), 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 Lang Graph, Lang Chain, Llama Index, 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.

• Languages:
  
  Node.js, JavaScript, Type Script, Python, Go
• Cloud: AWS/GCP
• Containers:
  Kubernetes, Docker
• APIs: REST, gRPC
• Messaging:
  Kafka or equivalent
• AI Frameworks:
  Lang Graph, Lang Chain, Llama Index, CrewAI, Semantic Kernel
• ML:
  Hugging Face, Py Torch
• Dev Ops: CI/CD, Terraform (preferred)