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Bridgetown Research

Bangalore, Karnataka, IndiaJob$10–35K/yrPosted 3mo agoStill listed 3 days ago

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At a glance

Compensation
$10–35K/yr
Location
Bangalore, Karnataka, India
Work Authorization
Not specified

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Job overview

The role seeks a backend engineer with 7+ years of experience who will design and scale distributed systems on AWS, architect multi-database solutions, manage infrastructure with Terraform, Docker, and Kubernetes, handle large-scale data pipelines, ensure security through threat modeling and compliance, and apply AI/LLM knowledge to product decisions while collaborating across teams in a fast-moving startup environment.

Skills & qualifications

RequiredNice to have

Skills

Node.jsTerraformDatabasesDynamoTypeScriptMaintenanceNoSQLNestJSAthenaThreat ModelingELTPostgresContinuous IntegrationElasticSearchAmazon Web ServicesContinuous DeliveryDockerSystem DesignCompliancePythonInfrastructureETLArtificial IntelligenceMicrosoft AzureDistributed SystemsKubernetesGoogle Cloud PlatformExpressAWSPostgreSQLDynamoDBCI/CDLLMsRAGEmbeddingsGCPAzure

Qualifications

7+ Years Backend Engineering Experience

Full job description

Requirements:

  • 7+ years of backend engineering experience with deep expertise in TypeScript/Node.js (NestJS, Express) and strong Python proficiency.
  • Proven track record of designing and scaling distributed systems in production on AWS.
  • Strong multi-database fluency - you've architected systems across relational (PostgreSQL), NoSQL (DynamoDB), search (Elasticsearch), and analytical stores (Athena) and can reason about when to use what.
  • Hands-on with Terraform, Docker, and Kubernetes - you've set up and maintained production infrastructure, not just used it.
  • Experience managing data at scale - schema evolution, ETL/ELT patterns, query performance, and lightweight warehouse maintenance.
  • Strong security posture - experience designing secure systems end-to-end, including auth architectures, threat modeling, secure CI/CD pipelines, and compliance-aware data handling.
  • Deep understanding of the AI landscape - LLMs, agentic patterns, function calling, RAG, embeddings - and the ability to translate that into engineering decisions.
  • Awareness of GCP and Azure architectures even if AWS is your primary.
  • Strong opinions on system design, loosely held - you make architectural tradeoffs explicit and drive alignment across teams.
  • Startup tempo - you ship fast without cutting corners on what matters.

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