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About The Company
Pearson is a global leader in education, providing innovative learning solutions that empower learners and educators worldwide. With a rich history of over a century, Pearson is committed to transforming the educational landscape through cutting-edge technology, digital content, and personalized learning experiences. The company operates across multiple regions, serving millions of students, teachers, and institutions, and continuously strives to enhance educational outcomes through innovation and excellence.
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 instrumental in designing, developing, deploying, and maintaining scalable AI and Machine Learning solutions that drive our digital learning platforms and services. Your expertise will be vital in implementing robust automation pipelines for model training, testing, deployment, and monitoring, ensuring seamless integration of AI capabilities into our products. Collaboration across cross-functional teams including Data Scientists, DevOps Engineers, Software Developers, and Cloud specialists will be essential to operationalize AI models in production environments effectively.
Qualifications
The ideal candidate will possess a minimum of 5 years of experience in AI/ML Engineering, Software Engineering, or MLOps, with a strong foundation in Python programming. Proven experience with CI/CD pipelines, especially using Jenkins, is required. Candidates should have hands-on experience with machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers, particularly in Generative AI and Large Language Model ecosystems. Familiarity with cloud platforms like AWS, Azure, or GCP, as well as containerization tools such as Docker and Kubernetes, is essential. Knowledge of databases including SQL, NoSQL, and vector databases, along with monitoring tools like Prometheus, Grafana, or CloudWatch, will be advantageous. Strong understanding of MLOps principles, deployment workflows, and secure credential management is necessary to succeed in this role.
Responsibilities
AI/ML Development
- Design, develop, and deploy advanced machine learning and AI solutions tailored for various business applications within the education sector.
- Build scalable, efficient AI systems utilizing Python and modern frameworks like TensorFlow, PyTorch, and Hugging Face Transformers.
- Develop and optimize applications involving NLP, Computer Vision, Recommendation Systems, Predictive Analytics, or Generative AI, including working with LLMs, prompt engineering, embeddings, vector databases, and RAG architectures where applicable.
- Fine-tune and evaluate machine learning models to ensure high performance, scalability, and accuracy, aligning with business needs.
MLOps & Automation
- Construct and maintain end-to-end MLOps pipelines for model training, validation, deployment, and ongoing monitoring to ensure operational excellence.
- Implement CI/CD pipelines using Jenkins to automate build, testing, and deployment processes for AI applications.
- Automate the entire model lifecycle management, including versioning, retraining, rollback, and performance monitoring.
- Integrate Jenkins with version control systems like Git, containerization tools such as Docker, orchestration platforms like Kubernetes, and infrastructure automation tools like Terraform.
- Guarantee reproducibility and reliability of AI workflows across development and production environments.
Cloud & Infrastructure
- Deploy and manage AI/ML workloads on cloud platforms including AWS, Azure, or GCP, ensuring high availability and scalability.
- Manage containerized AI applications utilizing Docker and Kubernetes for seamless deployment and scaling.
- Leverage infrastructure-as-code and orchestration tools to streamline deployment processes and resource management.
- Optimize GPU and compute resource utilization to enhance training and inference performance.
Data Engineering & Integration
- Collaborate with data teams to prepare, process, and validate structured and unstructured datasets for AI model training and evaluation.
- Build robust data pipelines and integrate AI services with enterprise systems and APIs to facilitate seamless data flow.
- Ensure data quality, governance, and security compliance throughout the data lifecycle.
Monitoring & Production Support
- Monitor AI systems in production for model drift, performance issues, and operational anomalies.
- Implement comprehensive logging, observability, alerting, and performance dashboards to facilitate proactive maintenance.
- Troubleshoot deployment and infrastructure issues promptly to minimize downtime and ensure system stability.
Collaboration & Leadership
- Mentor junior engineers, fostering a culture of continuous learning and best practices in AI engineering and DevOps.
- Work closely with cross-functional teams to translate business requirements into effective technical solutions.
- Participate in architecture reviews, sprint planning, and technical decision-making to align AI initiatives with organizational goals.
Benefits
At Pearson, we offer a comprehensive benefits package designed to support our employees' well-being and professional growth. Employees enjoy competitive salaries, health insurance, and retirement plans. We promote a flexible work environment with opportunities for remote work and flexible hours. Continuous learning is encouraged through access to training programs, conferences, and certifications. Additionally, employees benefit from a collaborative and innovative company culture that values diversity, inclusion, and work-life balance. We also provide various wellness programs and employee assistance resources to support overall health and happiness.
Equal Opportunity
Pearson is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive
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