Performance Tester

Innova ESI; Pune District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

Innova ESI in Pune is seeking a Performance Engineer to design, execute, and optimize performance testing for AI applications, with emphasis on AI agents, AI platforms, distributed systems, and performance engineering.

The role focuses on latency, throughput, scalability, and cost-performance, building automated test frameworks using JMeter, Locust, Gatling, k6, or Python-based tools, and shaping performance baselines, SLAs, and KPIs.

Qualifications

• 4+ years of experience in performance engineering, performance testing, load testing, or scalability engineering.
• Hands-on experience testing AI/ML applications, LLM applications, AI agents, or AI platforms.
• Strong understanding of distributed systems, microservices, APIs, cloud computing, and scalable architectures.
• Experience with REST APIs, JSON, HTTP, WebSockets, asynchronous processing, and API performance testing.
• Strong knowledge of Linux, networking, databases, caching, queues, and system performance concepts.

Responsibilities

• Design and implement performance testing strategies for AI agents and AI platforms.
• Develop performance test plans, workload models, test scenarios, and acceptance criteria for AI-powered applications.
• Test AI agent workflows involving LLMs, RAG, vector databases, tools, APIs, memory, orchestration, and multi-agent systems.
• Measure and optimize latency, throughput, concurrency, scalability, resource utilization, availability, and reliability.
• Profile applications and identify performance bottlenecks across application, database, network, infrastructure, model-serving, and cloud layers.

Skills

Performance engineering

Distributed systems

AI/ML applications

APIs

Cloud platforms

Tools

JMeter

Gatling

Locust

k6

LoadRunner

Python

Job description

We are seeking a Performance Engineer with strong experience in AI agents, AI platforms, distributed systems, and performance engineering to design, execute, and optimize performance testing for next-generation AI applications. The ideal candidate will have hands-on experience testing LLM-based applications, AI agent workflows, APIs, microservices, cloud platforms, and high-scale distributed systems.

Key Responsibilities

• Design and implement performance, load, stress, scalability, endurance, and capacity testing strategies for AI agents and AI platforms.
• Develop performance test plans, workload models, test scenarios, and acceptance criteria for LLM and AI-powered applications.
• Test AI agent workflows involving LLMs, RAG, vector databases, tools, APIs, memory, orchestration, and multi-agent systems.
• Measure and optimize latency, throughput, concurrency, scalability, resource utilization, availability, and reliability.
• Analyze LLM inference latency, token throughput, time-to-first-token (TTFT), time-per-output-token (TPOT), request latency, and cost/performance metrics.
• Build automated performance testing frameworks using tools such as JMeter, Gatling, Locust, k6, or custom Python-based frameworks.
• Generate realistic workloads for concurrent users, API requests, AI agent sessions, prompts, tokens, and multi-step agent workflows.
• Conduct performance testing of REST APIs, GraphQL APIs, microservices, event-driven systems, and asynchronous workloads.
• Profile applications and identify performance bottlenecks across application, database, network, infrastructure, model-serving, and cloud layers.
• Monitor and analyze systems using Prometheus, Grafana, ELK/OpenSearch, Datadog, New Relic, CloudWatch, Azure Monitor, or equivalent tools.
• Evaluate performance of AI infrastructure including GPU/CPU utilization, memory, network throughput, model serving, autoscaling, and containerized workloads.
• Collaborate with AI/ML engineers, software engineers, DevOps, SRE, architects, and product teams to resolve performance issues.
• Establish performance baselines, SLAs, SLOs, KPIs, capacity thresholds, and performance regression criteria.
• Integrate performance tests into CI/CD pipelines and support continuous performance monitoring.
• Perform root-cause analysis and provide actionable recommendations for improving AI platform scalability, reliability, and efficiency.
• Prepare performance reports, dashboards, bottleneck analysis, capacity forecasts, and executive-level performance summaries.

Required Skills

• 4+ years of experience in performance engineering, performance testing, load testing, or scalability engineering.
• Hands-on experience testing AI/ML applications, LLM applications, AI agents, or AI platforms.
• Strong understanding of distributed systems, microservices, APIs, cloud computing, and scalable architectures.
• Experience with performance testing tools such as JMeter, Gatling, Locust, k6, LoadRunner, or similar.
• Experience with REST APIs, JSON, HTTP, WebSockets, asynchronous processing, and API performance testing.
• Strong knowledge of Linux, networking, databases, caching, queues, and system performance concepts.
• Experience with observability and monitoring tools such as Prometheus, Grafana, ELK, Datadog, New Relic, or cloud-native monitoring solutions.
• Experience with Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP.
• Strong analytical and troubleshooting skills with the ability to identify performance bottlenecks and root causes.