LLM Techniques)

Aspen Technology•Bedford, MA

2d•Onsite

About The Position

The driving force behind our success has always been the people of AspenTech. What drives us, is our aspiration, our desire and ambition to keep pushing the envelope, overcoming any hurdle, challenging the status quo to continually find a better way. You will experience these qualities of passion, pride and aspiration in many ways — from a rich set of career development programs to support of community service projects to social events that foster fun and relationship building across our global community. The Role We are seeking a Senior Automation and AI-Driven Quality Engineer to design, implement, and scale intelligent quality, automation, and performance engineering solutions across enterprise-grade, AI-enabled platforms. This role is hands-on and technically deep, with strong ownership across test automation, performance engineering, AI-assisted quality workflows, and SAFe-based delivery pipelines. You will work closely with product engineering, architecture, DevOps, and data/AI teams to embed Built-In Quality into every stage of the SDLC.

Requirements

BS/MS in Computer Science, Engineering, or related field.
5 -8 years of experience in software engineering, with strong focus on automation, quality engineering, or performance engineering.
Strong programming skills in Python, Java, C#, or JavaScript/TypeScript.
Hands-on experience building test automation frameworks and integrating them into CI/CD pipelines.
Experience with cloud platforms (Azure), microservices, and distributed systems.
Solid understanding of Agile / SAFe delivery models and enterprise-scale software development.
Excellent problem-solving, communication, and cross-team collaboration skills.
Experience with AI/ML-assisted testing, LLM-based automation, or intelligent quality systems.
Familiarity with performance testing tools (e.g., JMeter, Gatling, K6) and observability stacks.
Experience with containers (Docker), orchestration (Kubernetes), and Infrastructure as Code.
Exposure to DevOps, reliability engineering, or production telemetry analysis.
Prior experience working in AI-powered or data-driven product environments.

Responsibilities

Design and implement AI-driven quality automation solutions to improve test coverage & defect detection.
Apply ML/LLM-assisted techniques for test generation, intelligent test selection, prioritization, and regression optimization.
Build automated quality signals that combine test results, telemetry, logs, and metrics to support data-driven release decisions.
Drive shift-left and shift-right quality strategies, integrating automation with both pre-release pipelines and production monitoring.
Architect and maintain scalable, modular automation frameworks for API, backend, and system-level testing using Python, Java, or TypeScript.
Implement automation across distributed systems, microservices, and AI-enabled components.
Integrate automation into CI/CD pipelines (e.g., Azure DevOps, GitHub Actions, Jenkins) with quality gates and release checks.
Develop reusable test utilities, data orchestration tools, and environment-aware automation for complex enterprise deployments.
Enable automation reporting and dashboards for engineering, QA, and leadership visibility.
Design and execute performance, load, and stress testing strategies for cloud-native and distributed systems.
Establish performance baselines, SLAs/SLOs, and continuously validate system behavior under real-world workloads.
Analyze performance bottlenecks using observability and APM tools (metrics, logs, traces).
Collaborate with architecture and DevOps teams to improve scalability, resilience, and system reliability.
Support production performance validation using telemetry-driven feedback loops.
Act as a senior technical contributor across Agile Release Trains (ARTs) in a SAFe environment.
Partner with Product Owners, Architects, and RTEs to embed automation, performance, and quality objectives into PI Planning.
Contribute to backlog refinement by defining automation and quality enablers.
Lead root cause analysis for complex quality, performance, and reliability issues across teams.
Mentor engineers on automation best practices, AI-assisted testing, and quality engineering principles.