AI Engineer, Vice President

Role Overview: 

We are looking for a forward-thinking AI Engineer to help redefine how quality is understood, measured, and improved across our engineering organization. This role goes beyond traditional test automation — it focuses on building intelligent systems that enhance decision-making, accelerate feedback loops, and elevate product quality through data-driven insights and adaptive tooling. 

You will work at the intersection of AI, software testing, and engineering operations, applying techniques from Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLMs) to create scalable solutions that support quality assurance, risk analysis, and continuous improvement. 
 

Key Responsibilities: 

• Design and implement AI-driven components that support quality engineering across the SDLC. 

• Develop NLP pipelines to extract insights from requirements, user feedback, and test artifacts. 

• Fine-tune and deploy LLMs to support intelligent test generation, summarisation, and anomaly detection. 

• Collaborate with QA, DevOps and Product teams to integrate AI tooling into CI/CD pipelines and quality gates. 

• Analyze historical test and defect data to identify patterns and optimize regression test coverage. 

• Maintain and improve model performance through continuous learning and feedback loops. 
   

Required Skills & Experience: 

• Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). 

• Hands-on experience with NLP techniques and libraries (e.g., spaCy, Hugging Face Transformers). 

• Proven experience in fine-tuning LLMs for domain-specific tasks. 

• Familiarity with software testing methodologies and QA lifecycle. 

• Ability to work with structured and unstructured data sources. 

• Excellent problem-solving and communication skills. 
   

Preferred Qualifications: 

• LLM Expertise: Experience fine-tuning and deploying large language models (e.g., GPT, LLaMA) for domain-specific tasks such as test case generation, summarisation, and anomaly detection. 

• Quality Engineering Knowledge: Familiarity with functional and regression testing principles, test case design, and QA lifecycle. 

• Automation Frameworks: Hands-on experience with tools like Selenium, Playwright, or Cypress, and integrating AI into these frameworks. 

• MLOps & Deployment: Exposure to MLOps practices including model versioning, monitoring, and CI/CD integration using platforms like MLflow, Kubeflow, or Azure ML. 

• Cloud & Infrastructure: Experience working with cloud platforms (Azure) and containerization tools (Docker, Kubernetes) for scalable AI deployment. 

• Data Engineering: Ability to work with large-scale datasets, including preprocessing, feature engineering, and data pipeline development. 

• Security & Compliance Awareness: Understanding of data privacy, model interpretability, and compliance standards relevant to AI in enterprise environments. 

• Collaboration & Communication: Proven ability to work cross-functionally with QA, DevOps, and product teams, and to communicate technical concepts to non-technical stakeholders. 

• Exposure to Gherkin syntax and behavior-driven development (BDD) practices.