About this role<div><div>Role Overview: </div><div>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. </div><div>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.   </div><div>Key Responsibilities: </div><div><ul><li>Design and implement AI-driven components that support quality engineering across the SDLC. </li></ul></div><div><ul><li>Develop NLP pipelines to extract insights from requirements, user feedback, and test artifacts. </li></ul></div><div><ul><li>Fine-tune and deploy LLMs to support intelligent test generation, summarisation, and anomaly detection. </li></ul></div><div><ul><li>Collaborate with QA, DevOps and Product teams to integrate AI tooling into CI/CD pipelines and quality gates. </li></ul></div><div><ul><li>Analyze historical test and defect data to identify patterns and optimize regression test coverage. </li></ul></div><div><ul><li>Maintain and improve model performance through continuous learning and feedback loops.   </li></ul></div><div>Required Skills &amp; Experience: </div><div><ul><li>Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn). </li></ul></div><div><ul><li>Hands-on experience with NLP techniques and libraries (e.g., spaCy, Hugging Face Transformers). </li></ul></div><div><ul><li>Proven experience in fine-tuning LLMs for domain-specific tasks. </li></ul></div><div><ul><li>Familiarity with software testing methodologies and QA lifecycle. </li></ul></div><div><ul><li>Ability to work with structured and unstructured data sources. </li></ul></div><div><ul><li>Excellent problem-solving and communication skills.   </li></ul></div></div><div><div>Preferred Qualifications: </div><div><ul><li>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. </li></ul></div><div><ul><li>Quality Engineering Knowledge: Familiarity with functional and regression testing principles, test case design, and QA lifecycle. </li></ul></div><div><ul><li>Automation Frameworks: Hands-on experience with tools like Selenium, Playwright, or Cypress, and integrating AI into these frameworks. </li></ul></div><div><ul><li>MLOps &amp; Deployment: Exposure to MLOps practices including model versioning, monitoring, and CI/CD integration using platforms like MLflow, Kubeflow, or Azure ML. </li></ul></div><div><ul><li>Cloud &amp; Infrastructure: Experience working with cloud platforms (Azure) and containerization tools (Docker, Kubernetes) for scalable AI deployment. </li></ul></div><div><ul><li>Data Engineering: Ability to work with large-scale datasets, including preprocessing, feature engineering, and data pipeline development. </li></ul></div><div><ul><li>Security &amp; Compliance Awareness: Understanding of data privacy, model interpretability, and compliance standards relevant to AI in enterprise environments. </li></ul></div><div><ul><li>Collaboration &amp; Communication: Proven ability to work cross-functionally with QA, DevOps, and product teams, and to communicate technical concepts to non-technical stakeholders. </li></ul></div><div><ul><li>Exposure to Gherkin syntax and behavior-driven development (BDD) practices. </li></ul></div><div> </div></div>Our benefits To help you stay energized, engaged and inspired, we offer a wide range of benefits including a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.Our hybrid work modelBlackRock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at BlackRock.About BlackRockAt BlackRock, we are all connected by one mission: to help more and more people experience financial well-being.  Our clients, and the people they serve, are saving for retirement, paying for their children’s educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.This mission would not be possible without our smartest investment – the one we make in our employees. It’s why we’re dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.For additional information on BlackRock, please visit <a href="http://careers.blackrock.com/" target="_blank">@blackrock</a> | Twitter: <a href="https://twitter.com/blackrock" target="_blank">@blackrock</a> | LinkedIn: <a href="http://www.linkedin.com/company/blackrock" target="_blank">www.linkedin.com/company/blackrock</a><div></div><div></div>BlackRock is proud to be an Equal Opportunity Employer.  We evaluate qualified applicants without regard to age, disability, family status, gender identity, race, religion, sex, sexual orientation and other protected attributes at law.