AI-Driven Test Automation Engineer

This position is responsible for the design, development, and implementation of AI-driven solutions to revolutionize connectivity test automation. The role focuses on leveraging Generative AI and Large Language Models (LLMs) to automate the entire testing lifecycle - from requirement analysis and test case generation to automated script synthesis and intelligent defect analysis. The engineer will bridge the gap between cutting-edge AI technologies and automotive connectivity systems, ensuring a highly efficient and intelligent validation process.

• AI-Driven Test Generation: Design and develop AI pipelines to automatically extract test logic from natural language requirements or technical specifications to generate structured test cases and acceptance criteria.
• Automated Script Synthesis: Architect and implement AI Agents capable of generating production-ready automation scripts (e.g., Python/Pytest) for connectivity features, significantly reducing manual scripting effort.
• Intelligent Issue Analysis: Develop and deploy AI Agents integrated with RAG systems to automate complex connectivity log analysis and Root Cause Analysis, enabling intelligent defect categorization and actionable fix suggestions.
• AI Framework Orchestration: Develop and maintain a scalable testing framework using LangChain or similar orchestration tools, integrating AI capabilities seamlessly into existing automation platforms and test management tools .
• Model Optimization & Fine-tuning: Perform fine-tuning (e.g., LoRA) and advanced Prompt Engineering on LLMs to adapt them to specialized automotive domains, ensuring high accuracy and reducing "hallucinations" in technical outputs.
• Tooling & Simulation: Develop and maintain AI-enhanced test tools and utilities, including intelligent simulation agents and virtual test environments, to enhance the testing process for connectivity features and system-level interactions.
• Evaluation & Optimization: Establish and refine comprehensive evaluation frameworks and KPIs (e.g., accuracy, latency,, Agent success rate) for AI products, driving continuous and data-driven optimization of system performance.
• Collaboration & Innovation: Collaborate with cross-functional teams to identify AI application scenarios. Stay at the forefront of AI research to continuously improve testing methodologies and efficiency.

Education Qualification:

• Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Engineering, or a related field.

No. of Years of Experience:

• 5+ years of experience in software testing or development, with at least 3 years of hands-on experience in AI/LLM application development or AI-driven automation.

Technical Skills:

• AI/LLM Expertise: Proficient in LangChain, Dify, or similar LLM application frameworks, strong understanding of RAG architectures, Agentic workflows and Vector Databases.
• Model Engineering: Experience in Fine-tuning LLMs and advanced Prompt Engineering, familiar with model deployment and inference optimization.
• Connectivity Knowledge: Deep understanding of automotive connectivity features and related components (e.g., IVI, ECG, TCU, Cloud, and Mobile App) and communication protocols (CAN, SOA, MQTT, TCP/IP, 4G/5G etc.).
• Programming: Strong programming skills in languages relevant to automation, AI or embedded systems, such as Python, JAVA, C/C++, or similar.
• Automation Frameworks: Proficient with automated testing tools and frameworks (e.g., Pytest, Appium), skilled in developing automation for Android systems (IVI) and mobile applications, covering both UI and system-level interactions
• DevOps: Experience in CI/CD pipelines and integrating AI tools into the software development lifecycle.

Functional Skills:

• Proven ability to translate complex automotive testing requirements into AI-solvable problems.
• Strong analytical and problem-solving skills applied to complex connectivity system issues and AI model performance.
• Ability to lead technical AI initiatives involving cross-functional teams and external partners.
• Excellent technical documentation and communication skills in English.

Behavioral Skills:

• Proactive, self-motivated, and demonstrates a strong sense of ownership over AI innovation.
• Excellent communication and interpersonal skills for effective collaboration with cross teams.
• Highly adaptable and capable of responding to the fast-paced evolution of AI technology.