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Open nowPosted 3 days ago

Senior Backend Software Engineer

Weekday AI

Pay
₹30 an hour
Where
Bengaluru, KA, India
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Your applicationOpen nowSenior Backend Software EngineerWeekday AI · Bengaluru, KA, India

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The clock on this job

Early applications get read.

13.1% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 2.2%1 day
  2. 4.7%3 days
  3. 13.1%7 days
  4. 21.1%14 days
  5. 35.7%30 days
  6. 58.7%60 days
This job: posted 3 days ago

The posting

This role is for one of Weekday’s clients Salary range: Rs 3000000 - Rs 9000000 (ie INR 30 - 90 LPA)

Min Experience: 5+ years Location: Bengaluru JobType: full-time

We are looking for an experienced Senior Backend Software Engineer to design, develop, and operate highly scalable, secure, and reliable backend systems. The role involves architecting low-latency, real-time microservices that process and detect sensitive data at scale while ensuring high availability, performance, and security.

The ideal candidate will have strong expertise in distributed systems, backend engineering, data processing, and cloud infrastructure. You will contribute to the architecture of mission-critical services, real-time data ingestion pipelines, machine learning inference systems, and advanced search and agentic AI solutions. This role requires a hands-on engineer with proven technical leadership and the ability to solve complex business and engineering challenges.

Requirements

Key Responsibilities

1. Backend Architecture and Development

  • Design, build, and maintain highly available, secure authentication systems and API services.
  • Develop and operate low-latency microservices capable of handling high-throughput workloads.
  • Maintain and evolve mission-critical internal databases, backend services, and distributed architectures.
  • Ensure production systems meet stringent standards for reliability, scalability, performance, and security.

2. Distributed Systems and Infrastructure

  • Architect, optimise, and operate high-volume, auto-scaling infrastructure for real-time data ingestion and processing.
  • Build and maintain systems that process substantial data volumes, including several terabytes per day, or support millions of users.
  • Develop and improve machine learning inference infrastructure and other latency-sensitive backend services.
  • Implement monitoring, observability, and reliability practices for production-grade distributed systems.
  • Contribute to cloud infrastructure and deployment automation using modern DevOps tools and practices.

3. Search and Agentic AI Systems

  • Design and develop scalable search systems and agentic workflows capable of supporting high levels of concurrency.
  • Improve large language model (LLM) accuracy, cost efficiency, and scalability through effective prompt and context engineering.
  • Work on capabilities such as data ingestion, text summarisation, semantic search, traditional search, and agent orchestration.
  • Optimise system performance and resource utilisation for AI-powered applications.

4. Data Processing and Database Management

  • Build and maintain large-scale and real-time data processing pipelines.
  • Work with relational and NoSQL databases to support high-volume, distributed workloads.
  • Design and optimise data processing architectures using technologies such as PostgreSQL, Kafka, Flink, Snowflake, Hadoop, and ZooKeeper.
  • Ensure data systems deliver reliable ingestion, processing, storage, and retrieval at scale.

5. Observability and Documentation

  • Build scalable observability systems that provide detailed insights into customer usage, system performance, and service reliability.
  • Establish effective monitoring and troubleshooting practices across distributed services.
  • Create and maintain technical documentation for internal systems, public APIs, and backend services.

6. Technical Leadership and Collaboration

  • Lead the decomposition and resolution of complex technical and business problems.
  • Drive architectural decisions and contribute to engineering best practices.
  • Mentor engineers and collaborate with cross-functional teams to deliver reliable, production-ready solutions.
  • Take ownership of system design, implementation, deployment, and ongoing optimisation.

Required Skills and Qualifications

  • 6+ years of professional software engineering experience, with demonstrated technical leadership and expertise in scalable, distributed architectures.
  • Strong proficiency in one or more programming languages, such as Python, Go, Java, or C++, with the willingness to learn additional technologies.
  • Strong foundation in computer science fundamentals, data structures, and algorithms.
  • Proven experience building and operating scalable systems handling thousands of requests per second (RPS), with high reliability targets such as 99.99% availability.
  • Experience designing complex distributed systems that process substantial data volumes or support millions of users.
  • Hands-on experience developing low-latency, high-throughput microservices and deploying production-grade systems.
  • Strong knowledge of distributed storage and database technologies, including SQL and NoSQL databases such as PostgreSQL and Cassandra.
  • Experience building and maintaining large-scale or real-time data processing pipelines.
  • Strong understanding of production monitoring, observability, performance optimisation, and system reliability.
  • Ability to break down complex business requirements into scalable technical solutions and lead implementation effectively.

Must-Have Skills

  • Python
  • Go
  • Java

Good-to-Have Skills

  • Node.js
  • React
  • Redis

Technology Stack

The engineering environment includes:

  • Programming Languages: Go, Python, Node.js
  • Frontend Technologies: React
  • Databases and Storage: Cassandra, Redis, SQL databases
  • Cloud and Infrastructure: AWS, Terraform
  • Containerisation and Orchestration: Docker, Kubernetes
  • Streaming and Networking: Kafka, Envoy

Preferred Experience

  • Experience building large-scale search or agentic AI systems.
  • Familiarity with semantic search, text summarisation, prompt engineering, context engineering, and agent orchestration.
  • Experience with machine learning inference systems and real-time data ingestion infrastructure.
  • Knowledge of distributed data processing technologies such as Flink, Snowflake, Hadoop, and ZooKeeper.
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