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- Tasks: Lead load testing for a critical system release and design automated tests.
- Company: Join Monolith AI, a fast-paced tech company focused on innovation.
- Benefits: Competitive pay, flexible work hours, and a chance to make an impact.
- Why this job: Be at the forefront of performance testing in a dynamic environment.
- Qualifications: 4+ years in QA, experience with load testing tools, and strong Python skills.
- Other info: Fast-paced 2-month contract with opportunities for growth and learning.
The predicted salary is between 500 - 1500 ÂŁ per month.
Monolith AI is seeking an experienced QA Engineer to lead load testing efforts for a critical system release focused on improving concurrency and high request load handling. This fast-paced, short-term engagement requires someone who can quickly understand complex distributed systems, design comprehensive load tests, and work collaboratively with a rapidly growing engineering team to ensure our new environment meets performance requirements.
Primary Responsibilities
- Design and Implement Automated Load Testing Framework
- Develop comprehensive load tests for FastAPI endpoints, Temporal workflows/activities, and AWS service interactions.
- Create realistic test scenarios simulating concurrent workflow execution patterns, including graph-based workflow orchestration.
- Build automated test suites that measure system behavior under varying concurrency levels and request loads.
- Performance Analysis and Bottleneck Identification
- Monitor and analyze system performance across the entire stack (API layer, Temporal workers, AWS services).
- Identify concurrency limitations in Temporal workflow execution, AWS service limits (Athena, ECS), and inter‑component communication.
- Document performance characteristics including response times, throughput limits, and failure modes under load.
- Collaborate on Non‑Functional Requirements (NFR) Definition
- Work with Customer Success and Product teams to understand business requirements and translate them into measurable performance criteria.
- Iterate on acceptable concurrency thresholds, latency targets, and throughput requirements.
- Validate that proposed NFRs are realistic and achievable given architectural constraints.
- System Documentation and Knowledge Extraction
- Understanding of the existing system through code review, discussions with the development team, and exploratory testing.
- Create clear documentation of test methodologies, results, and recommendations for future testing.
- Recommendation and Optimization Guidance
- Provide actionable recommendations for removing identified bottlenecks.
- Suggest configuration optimizations for Temporal (worker pools, task queues) and AWS services (Athena concurrency, ECS capacity).
- Rapid Communication and Status Reporting
- Maintain daily/frequent communication with the Tech Lead regarding project progress, blockers, and findings.
- Quickly elevate issues that could impact the aggressive timeline.
- Present findings and recommendations to technical and non‑technical stakeholders.
- Cross‑Component Integration Testing
- Test complex scenarios involving graph execution triggering node workflows across multiple system boundaries.
- Validate S3 read/write operations under concurrent load.
- Ensure inter‑component communication (API → Temporal, Temporal Activity → API triggers) performs reliably at scale.
Key Performance Indicators
- Test Coverage and Execution
- Complete automated load test suite covering all critical components within first 3 weeks.
- Execute baseline and progressive load tests identifying maximum sustainable concurrency levels.
- Bottleneck Identification and Impact
- Identify and document top 5‑7 performance bottlenecks with clear impact analysis.
- Provide actionable remediation recommendations with estimated effort and impact for each bottleneck.
- NFR Definition and Validation
- Collaborate with stakeholders to define measurable NFRs within first 2 weeks.
- Validate that the system meets or document gaps against agreed NFR criteria by project end.
- Documentation and Knowledge Transfer
- Deliver comprehensive test documentation, results analysis, and system performance characteristics.
- Conduct knowledge transfer sessions ensuring team can maintain and extend testing framework.
- Project Velocity and Communication
- Meet weekly milestone targets in this fast‑paced 2‑month engagement.
- Maintain proactive communication rhythm (daily stand‑ups, weekly detailed reports to Tech Lead).
Required Qualifications
- Experience:
- 4+ years of experience in QA/performance testing roles.
- 2+ years of hands‑on experience with load testing distributed systems and microservices architectures.
- Proven experience with load testing tools (e.g., k6, JMeter, Locust, Gatling, Artillery).
- Experience testing workflow orchestration systems (Temporal, Airflow, Prefect, or similar).
- Demonstrated ability to test systems integrating with AWS services (particularly Athena, ECS, S3).
- Technical Skills:
- Strong proficiency in Python (required for test automation and working with FastAPI, Temporal).
- Experience with REST API testing and performance validation.
- Understanding of distributed systems concepts: concurrency, queueing, backpressure, rate limiting.
- Familiarity with AWS infrastructure and service limits.
- Experience with monitoring and observability tools (Prometheus, Grafana, Datadog, or similar).
- Proficiency with Git and CI/CD pipelines.
- Ability to read and understand code in order to design effective tests.
- Immediate Availability:
- Ability to start in early January 2025 and commit to focused 3‑month engagement.
- Availability for full‑time contract work during project duration.
Preferred Qualifications
- Direct experience with Temporal (workflows, activities, workers).
- Experience with containerized workloads and Docker/ECS.
- Prior work in fast‑paced startup or scale‑up environments.
- Experience with infrastructure‑as‑code (Terraform, CloudFormation).
- Background in Site Reliability Engineering (SRE) or DevOps practices.
- Previous contract/consulting experience with rapid knowledge acquisition.
- Experience with graph‑based workflow systems or DAG execution engines.
- Knowledge of AWS service limits and optimization strategies.
Essential Soft Skills
- Self‑Direction and Initiative: Ability to operate independently in an ambiguous, fast‑moving environment with minimal documentation; Proactive problem‑solving mindset; Comfortable making pragmatic decisions quickly in a time‑constrained project.
- Communication and Collaboration: Exceptional communication skills for extracting knowledge through conversations with existing team members; Ability to translate technical findings into clear, actionable recommendations for diverse audiences; Comfortable asking clarifying questions and challenging assumptions respectfully; Strong written communication for documentation and status updates.
- Adaptability and Learning Agility: Quick learner who can rapidly understand complex, poorly documented systems; Flexible and comfortable with changing priorities in a 15‑person team that is doubling in size; Thrives in fast‑paced environments with aggressive timelines; Comfortable with 'good enough' when perfection isn’t achievable under constraints.
- Pragmatism and Results Orientation: Focused on delivering practical, actionable outcomes within tight timeframes; Understands balance between thoroughness and speed in a 2‑month engagement; Comfortable with 'good enough' when perfect isn’t achievable within constraints.
- Stakeholder Management: Skilled at managing expectations with technical leadership about realistic timelines and trade‑offs; Diplomatic when delivering difficult news about performance limitations or bottlenecks; Collaborative approach when working with CS and Product on NFR definition.
Key Challenges in This Role
- Rapid Knowledge Acquisition with Limited Documentation: The existing system lacks comprehensive documentation; requires quick building of understanding through code review, system exploration, and frequent discussions with the development team. Success requires comfort with ambiguity and strong investigative skills.
- Aggressive Timeline with High Impact: A 3‑month timeline to design tests, execute comprehensive load testing, identify bottlenecks, and deliver actionable recommendations is extremely tight. Must balance thoroughness with pragmatism; prioritize ruthlessly to ensure critical areas are covered.
- Complex Distributed System with Multiple Integration Points: The system involves multiple layers (FastAPI, Temporal, AWS services) with complex inter‑component communication patterns (graph → node workflows). Must understand the entire stack to design realistic, comprehensive load tests that expose real‑world bottlenecks.
QA Engineer - Load Testing Specialist (2 months contract) in London employer: Monolithai
Contact Detail:
Monolithai Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land QA Engineer - Load Testing Specialist (2 months contract) in London
✨Tip Number 1
Network, network, network! Get out there and connect with people in the industry. Attend meetups, webinars, or even just chat with folks on LinkedIn. You never know who might have a lead on that perfect QA Engineer role!
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your load testing projects or any relevant work you've done. This can really set you apart from other candidates and give potential employers a taste of what you can do.
✨Tip Number 3
Don’t be shy about reaching out directly to companies you’re interested in. A quick email or message expressing your interest can go a long way. Plus, applying through our website gives you a better chance of being noticed!
✨Tip Number 4
Prepare for interviews by brushing up on your knowledge of distributed systems and load testing tools. Be ready to discuss your experience with AWS services and how you've tackled performance bottlenecks in the past. Confidence is key!
We think you need these skills to ace QA Engineer - Load Testing Specialist (2 months contract) in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV is tailored to the QA Engineer role. Highlight your experience with load testing, especially with tools like k6 or JMeter, and any relevant projects that showcase your skills in distributed systems.
Craft a Compelling Cover Letter: Your cover letter should tell us why you're the perfect fit for this role. Mention specific experiences that relate to the job description, like your work with AWS services or performance testing, and show your enthusiasm for the position.
Showcase Your Technical Skills: Don’t forget to list your technical skills clearly. We want to see your proficiency in Python, REST API testing, and any monitoring tools you’ve used. This helps us quickly gauge your fit for the role.
Apply Through Our Website: We encourage you to apply through our website for a smoother application process. It’s the best way for us to receive your application and keep track of it, so don’t miss out on that!
How to prepare for a job interview at Monolithai
✨Know Your Tools
Make sure you’re familiar with load testing tools like k6, JMeter, or Locust. Brush up on how to use them effectively, as you'll likely be asked about your experience and how you would apply these tools to the role.
✨Understand the System
Dive deep into the concepts of distributed systems and microservices. Be prepared to discuss how concurrency, queueing, and rate limiting work, especially in relation to AWS services like Athena and ECS.
✨Prepare for Performance Analysis
Think about how you would approach identifying bottlenecks in a system. Have examples ready from your past experiences where you successfully analysed performance issues and provided actionable recommendations.
✨Communicate Clearly
Practice explaining complex technical concepts in simple terms. You’ll need to communicate findings to both technical and non-technical stakeholders, so being able to translate your insights is key.
