Staff Performance Engineer

MontyCloud; Bengaluru; On-site; INR 4,200,000 - 7,000,000; Full time

Job summary

MontyCloud in Bengaluru seeks an experienced Staff Performance Engineer to lead performance engineering for our cloud-native SaaS platform. You will drive strategy, build scalable performance platforms, and partner with SRE and architecture teams to optimize latency, scalability, and cost efficiency.

You will own production performance, implement AI-driven observability, and mentor engineers across teams, embedding performance into the full software lifecycle from development to production.

Qualifications

• 8+ years in performance engineering in large-scale SaaS or cloud-native environments.
• Experience with performance testing tools: JMeter, Gatling, Locust, or similar.
• Hands-on with serverless architectures (AWS Lambda, API Gateway).
• Proficient in observability tools: CloudWatch, Datadog, New Relic.
• Built performance engineering frameworks or platforms at scale.
• Experience tuning performance in distributed and serverless systems.
• CI/CD integration for performance engineering.
• Programming/scripting in Python or Java.
• AI/ML-based optimization techniques in performance.

Responsibilities

• Define and drive organization-wide performance strategy aligned with KPIs, customer experience, and cost efficiency.
• Architect and build scalable, self-service performance platforms enabling teams to run tests and analysis independently.
• Design and implement AI-driven performance solutions including anomaly detection, predictive insights, adaptive load testing, and automated optimization recommendations.
• Lead the design and execution of advanced performance testing strategies for serverless, distributed, and event-driven systems.
• Establish and standardize performance benchmarks, SLAs, SLOs, and KPIs across services.
• Drive integration of performance testing and validation into CI/CD pipelines (shift-left).
• Analyze system-wide performance bottlenecks including latency, cold starts, concurrency, and resource utilization.
• Collaborate with engineering, SRE, and architecture teams to influence system design for scalability and resilience.
• Own performance in production environments with observability tools and real-time monitoring.
• Implement intelligent observability using CloudWatch, Datadog, New Relic, and AI-based monitoring platforms.
• Lead capacity planning and scalability initiatives for high-throughput globally distributed systems.
• Drive cost-performance optimization strategies in cloud-native environments (FinOps).
• Mentor engineers across teams to promote a performance-first culture.
• Stay updated with AI/ML-driven optimization and cloud-native innovations.

Skills

Performance engineering

SaaS / cloud-native

AWS Lambda / Serverless

CI/CD integration

Python / Java

AI/ML optimization

Capacity planning

Cost optimization

Observability concepts

Cloud cost optimization

Education

Bachelor’s or Master’s degree in Computer Science/Engineering

Equivalent practical experience

Tools

JMeter

Gatling

Locust

k6

Job description

We are seeking an experienced Staff Performance Engineer to lead and scale performance engineering practices for our cloud-native SaaS platform. This role is responsible for driving performance, scalability, reliability, and cost efficiency at an organizational level, with a strong focus on serverless and distributed architectures.

You will define performance engineering strategy, build scalable and AI-driven performance platforms, and influence architectural decisions across teams. The role requires deep expertise in modern cloud environments and a strong focus on embedding performance into the entire software lifecycle, from development to production.

Key Responsibilities

• Define and drive organization-wide performance engineering strategy aligned with business KPIs, customer experience, and cost efficiency
• Architect and build scalable, self-service performance engineering platforms enabling teams to run performance tests and analysis independently
• Design and implement AI-driven performance engineering solutions including anomaly detection, predictive performance insights, adaptive load testing, and automated optimization recommendations
• Lead the design and execution of advanced performance testing strategies for serverless, distributed, and event-driven systems
• Establish and standardize performance benchmarks, SLAs, SLOs, and KPIs across services
• Drive integration of performance testing and validation into CI/CD pipelines to enable continuous performance engineering (shift-left approach)
• Analyze system-wide performance bottlenecks including latency, cold starts, concurrency limits, and resource utilization across distributed systems
• Collaborate with engineering, SRE, and architecture teams to influence system design for scalability, resilience, and performance optimization
• Own performance in production environments by leveraging observability tools, distributed tracing, and real-time monitoring systems
• Implement intelligent observability solutions using tools such as CloudWatch, Datadog, New Relic, and AI-based monitoring platforms
• Lead capacity planning and scalability initiatives for high-throughput and globally distributed systems
• Drive cost-performance optimization strategies in cloud-native environments (FinOps alignment)
• Mentor and guide engineers across teams, promoting a performance-first culture and best practices
• Stay updated with emerging trends in performance engineering, including AI/ML-driven optimization and cloud-native innovations

Desired Skill and Requirements

Must Have

• 8+ years of experience in performance engineering within large-scale SaaS or cloud-native environments
• Performance testing tools - JMeter, Gatling, Locust, or similar
• Serverless architectures - AWS Lambda, API Gateway, event-driven systems
• Performance monitoring and observability tools - CloudWatch, Datadog, New Relic, distributed tracing systems
• Building performance engineering frameworks or platforms at scale
• Performance optimization in distributed and serverless systems - latency, cold starts, concurrency, and scaling behavior
• Integration of performance engineering into CI/CD pipelines
• Programming/scripting - Python (preferred), Java, or similar
• AI/ML-based performance optimization techniques - anomaly detection, predictive analysis, adaptive load modeling
• Cloud platforms (AWS preferred) and performance optimization techniques
• Ability to identify and resolve complex performance bottlenecks
• Large-scale load testing and capacity planning
• Cost-performance optimization in cloud environments

Good To Have

• Kubernetes, containerized, and serverless architectures
• Chaos engineering and resilience testing
• Internal developer platforms and self-service tooling
• FinOps and cloud cost optimization strategies
• Globally distributed and multi-region architectures
• API performance optimization
• AIOps platforms and intelligent observability systems

Soft Skills

• Strong problem-solving and analytical thinking
• Ability to influence architectural and technical decisions across teams
• Excellent communication and stakeholder management skills
• Ownership mindset with the ability to drive cross-functional initiatives
• Mentorship and leadership capabilities
• Ability to operate in a fast-paced, high-growth SaaS environment

Experience

• 8+ years of experience in performance engineering in large-scale SaaS or cloud-native environments
• 3+ years of experience in Senior, Lead, or Staff-level performance engineering roles
• 4+ years of experience performance testing large-scale SaaS or distributed systems
• 5+ years of hands-on experience with performance testing tools such as JMeter, Gatling, k6, or Locust
• Experience designing and executing large-scale performance tests in production-like environments
• Experience identifying and resolving performance bottlenecks across application, database, network, and infrastructure layers
• Experience tuning databases for performance at scale
• Experience defining and implementing performance benchmarks, KPIs, and capacity planning strategies
• Experience working with observability and monitoring platforms for performance analysis
• Experience optimizing event-driven and serverless architectures
• Experience influencing architecture and engineering decisions across teams and domains
• Experience operating in fast-paced, high-growth SaaS environments

Education

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field
• Equivalent practical experience in performance engineering or cloud-native systems