Staff Test Engineer

Tally Solutions; Bengaluru; On-site; INR 2,400,000 - 3,600,000; Full time

Job summary

Tally Solutions is seeking a Backend Lead for its AI team in Bengaluru. The successful candidate will guide backend engineering efforts, ensuring performance and scalability while mentoring engineers.

You will drive end-to-end deployment testing, maintain CI/CD pipelines, and explore distillation techniques to optimize models for production. Strong leadership and deep Python expertise are required.

Qualifications

• Hands-on automation experience with Playwright, Selenium or Cypress.
• Programming skills in Python, Java, JavaScript or TypeScript.

Responsibilities

• End-to-End Deployment Testing: collaborate with backend engineers to deploy models via Docker/Kubernetes and design end-to-end test strategies (on-prem and cloud) grounded in system architecture.
• CI/CD Automation: maintain robust CI/CD pipelines and drive continuous test automation to minimize turnaround time for new releases.
• Research Distillation: actively research state-of-the-art open-source models (e.g., Qwen3, Mistral) and apply Knowledge Distillation to create smaller, faster proprietary models.
• Monitor Improve: continuously monitor drift, hallucination rates, and retrieval quality in production systems.
• Code Quality and Best Practices: ensure that code adheres to best practices, including clean code principles, testing, and documentation.
• Build Agentic Systems: implement multi-agent workflows using LangGraph to solve complex, multi-step user queries.
• Optimize for Scale: implement Quantization and vLLM/SGLang strategies to reduce inference costs and latency in production.

Skills

Python

Backend development

Leadership

Test automation

Docker

Kubernetes

API testing

Cloud

Tools

Playwright

Selenium

Cypress

Job description

Job Summary

We are seeking a highly skilled and experienced Backend Lead to join our AI team. As a Backend Lead, you will play a crucial role in ensuring the performance, scalability, and efficiency of our backend architecture while mentoring and leading a team of engineers. This position requires deep expertise in backend engineering, particularly in Python, and a strong understanding of building scalable and efficient systems. The ideal candidate will have a proven track record of leadership in backend development, and the ability to drive complex technical initiatives.

Responsibilities

• End-to-End Deployment Testing: Collaborate with backend engineers to deploy models via Docker/Kubernetes, and design end-to-end test strategies (on-prem and cloud) grounded in system architecture.
• CI/CD Automation: Maintain robust CI/CD pipelines and drive continuous test automation to minimize turnaround time for new releases.
• Research Distillation: Actively research state-of-the-art open-source models (e.g., Qwen3, Mistral) and apply Knowledge Distillation to create smaller, faster proprietary models.
• Monitor Improve: Continuously monitor drift, hallucination rates, and retrieval quality in production systems.
• Code Quality and Best Practices: Ensure that code adheres to best practices, including clean code principles, testing, and documentation.
• Build Agentic Systems: Implement multi-agent workflows using LangGraph to solve complex, multi-step user queries.
• Optimize for Scale: Implement Quantization and vLLM/SGLang strategies to reduce inference costs and latency in production.

Requirements

• Strong understanding of STLC, Agile and modern Quality Engineering practices, with expertise in functional, integration, API, regression, end to end, non-functional testing and compatibility testing.
• Ability to analyse requirements and system workflows, design effective test strategies, test scenarios, and risk-based test cases, and ensure comprehensive functional and non-functional coverage.
• Hands-on automation experience with Playwright/Selenium/Cypress and proficiency in Python, Java, JavaScript or TypeScript.
• Strong API and performance testing experience using tools such as Postman, REST Assured, JMeter, Gatling or k6.
• Good understanding of microservices, distributed systems, networking, databases and cloud-native architectures.
• Hands-on exposure to AWS/Azure/GCP, Docker/Kubernetes, CI/CD pipelines and Git-based workflows.
• Experience with observability, debugging and RCA using logs, metrics and monitoring tools such as Grafana, CloudWatch or ELK.
• Exposure to AI-assisted testing/development and testing of AI/LLM-based applications is an added advantage.