Backend Engineer at Weekday AI

Chennai, tamil nadu, India -

Full Time

Start Date

Immediate

Expiry Date

17 May, 26

Salary

1800000.0

Posted On

16 Feb, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Nodejs, PostgreSQL, Microservices, Docker, Kubernetes, Aws, Azure, Gcp, TypeScript, JavaScript, Elasticsearch, Kafka, Rabbitmq, Sqs, Grafana, Prometheus

Industry

technology;Information and Internet

Description

This role is for one of the Weekday's clients Salary range: Rs 1800000 (ie INR 18 LPA) Min Experience: 8 years Location: Bangalore, Chennai JobType: full-time We are seeking a highly experienced Senior Backend Engineer to design and scale enterprise-grade, AI-powered applications focused on knowledge automation and conversational intelligence. In this role, you will architect high-performance backend systems that power intelligent search, semantic understanding, and large-scale data processing. You will work closely with AI, platform, and DevOps teams to ensure seamless integration of machine learning capabilities into production systems while maintaining the highest standards of scalability, reliability, and security. This position demands deep expertise in backend architecture, distributed systems, and performance optimization within high-scale SaaS or AI-driven environments. Key Responsibilities Architect, design, and develop scalable backend systems using TypeScript and JavaScript (Node.js) Lead schema design, query optimization, and indexing strategies using PostgreSQL and/or Elasticsearch for high-volume workloads Build and maintain search engines, analytics systems, and data pipelines supporting AI/ML and NLP-driven features Optimize backend APIs for high throughput, low latency, and horizontal scalability across global deployments Establish best practices for code quality, observability, CI/CD pipelines, and performance monitoring Integrate machine learning inference services, semantic search, and vector indexing into production-grade systems Ensure reliability, fault tolerance, and data consistency across distributed microservices architectures Mentor engineers, conduct architecture and design reviews, and contribute to long-term technical roadmaps Collaborate with DevOps teams to manage containerized deployments using Docker and Kubernetes Implement secure coding practices, API authentication/authorization, rate limiting, and database access controls Continuously monitor, profile, and improve system performance across environments What Makes You a Great Fit 8–10 years of backend engineering experience in high-scale SaaS or AI-driven product environments Advanced expertise in PostgreSQL (schema design, indexing, performance tuning) and/or Elasticsearch (clustering, shard management, advanced queries) Strong proficiency in Node.js with TypeScript/JavaScript Proven experience scaling backend systems serving millions of transactions and large user bases Deep understanding of distributed systems, caching strategies, load balancing, and system design principles Hands-on experience with message queues and event-driven architectures such as Kafka, RabbitMQ, or SQS Experience with Docker, Kubernetes, and CI/CD tools like GitHub Actions, Jenkins, or Azure DevOps Working knowledge of cloud platforms including AWS, Azure, or GCP Familiarity with infrastructure as code and monitoring stacks such as Grafana, Prometheus, or ELK Strong knowledge of API security, authentication, and authorization mechanisms for REST or GraphQL systems Excellent debugging, problem-solving, and performance optimization skills Strong collaboration and mentoring capabilities within cross-functional engineering teams Key Skills nodejs, PostgreSQL, Microservices, Docker, Kuberenetes, AWS, azure, GCP

Responsibilities

The role involves architecting, designing, and developing scalable backend systems using TypeScript and Node.js, focusing on enterprise-grade, AI-powered applications for knowledge automation and conversational intelligence. Key tasks include leading schema design in PostgreSQL/Elasticsearch, building search engines and data pipelines, and integrating machine learning inference services into production systems.