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About The Company
Pearson is a global leader in education, providing innovative learning solutions and services to students, educators, and institutions worldwide. With a rich history of fostering educational excellence, Pearson leverages cutting-edge technology to enhance learning experiences and empower learners to achieve their full potential. The company is committed to transforming education through digital innovation, data-driven insights, and collaborative partnerships, making quality education accessible to diverse audiences across the globe.
About The Role
We are seeking a highly skilled and experienced AI Engineer to join our dynamic team at Pearson. In this role, you will be at the forefront of developing and operationalizing advanced AI and machine learning solutions that drive our educational products and services. Your expertise will be pivotal in designing scalable AI systems, implementing robust MLOps pipelines, and automating deployment workflows using Jenkins and other industry-standard tools. The ideal candidate will work closely with Data Scientists, DevOps Engineers, Software Developers, and Cloud specialists to ensure seamless integration and deployment of AI models in production environments.
Qualifications
The ideal candidate should possess a minimum of five years of experience in AI/ML engineering, software development, or MLOps. Strong programming skills in Python are essential, along with hands-on experience in building and deploying machine learning models using frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Familiarity with Generative AI, Large Language Models, and prompt engineering is highly desirable. Candidates should have a solid understanding of CI/CD pipelines, particularly with Jenkins, and experience integrating these pipelines with Docker, Kubernetes, and cloud platforms like AWS, Azure, or GCP. Knowledge of databases, including SQL, NoSQL, and vector databases, as well as monitoring tools such as Prometheus, Grafana, and CloudWatch, is also required.
Responsibilities
AI/ML Development
- Design, develop, and deploy innovative machine learning and AI solutions tailored for educational applications.
- Build scalable AI systems leveraging Python and modern frameworks, ensuring robustness and efficiency.
- Develop and optimize NLP, Computer Vision, Recommendation Systems, Predictive Analytics, and Generative AI applications.
- Work with Large Language Models, prompt engineering, embeddings, vector databases, and Retrieval-Augmented Generation (RAG) architectures where applicable.
- Fine-tune and evaluate models to meet performance, scalability, and accuracy benchmarks.
MLOps & Automation
- Construct and maintain end-to-end MLOps pipelines for model training, validation, deployment, and ongoing monitoring.
- Implement CI/CD pipelines using Jenkins to automate build, test, and deployment processes for AI applications.
- Automate lifecycle management tasks such as versioning, retraining, rollback, and performance monitoring.
- Integrate Jenkins with Git repositories, Docker containers, Kubernetes clusters, and infrastructure-as-code tools like Terraform.
- Ensure reproducibility, reliability, and scalability of AI workflows and deployments.
Cloud & Infrastructure
- Deploy and manage AI/ML workloads on cloud platforms including AWS, Azure, or Google Cloud Platform.
- Manage containerized applications using Docker and orchestrate them with Kubernetes for scalability and resilience.
- Utilize infrastructure-as-code and orchestration tools to streamline deployment processes.
- Optimize GPU and compute resource utilization for training and inference tasks to maximize efficiency.
Data Engineering & Integration
- Collaborate with data teams to prepare, process, and validate structured and unstructured datasets for AI model training.
- Build and maintain data pipelines, ensuring seamless integration of AI services with enterprise systems and APIs.
- Maintain high standards of data quality, governance, and security compliance throughout all processes.
Monitoring & Production Support
- Monitor AI systems in production for issues such as model drift, performance degradation, and operational anomalies.
- Implement logging, observability, alerting, and dashboards to facilitate proactive system management.
- Troubleshoot and resolve deployment and infrastructure issues promptly to ensure high availability and performance.
Collaboration & Leadership
- Mentor junior engineers and promote best practices in AI engineering and DevOps methodologies.
- Collaborate with cross-functional teams to translate business needs into effective technical solutions.
- Participate in architecture reviews, sprint planning, and technical decision-making to drive innovation and quality.
Benefits
At Pearson, we offer a comprehensive benefits package designed to support our employees' well-being and professional growth. This includes competitive salary packages, health insurance, retirement plans, and paid time off. Employees have access to continuous learning opportunities through training programs, workshops, and conferences. We foster a collaborative and inclusive work environment that encourages innovation and recognizes individual contributions. Additionally, our flexible work arrangements aim to promote work-life balance, ensuring our team members can thrive both professionally and personally.
Equal Opportunity
Pearson is an equal opportunity employer committed to fostering an inclusive environment for all employees. We do not discriminate based on race, ethnicity, gender, age, sexual orientation
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