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As part of a large-scale platform modernization initiative, the organization is strengthening its Artificial Intelligence and Machine Learning capabilities within a cloud-based platform environment. The goal is to deliver intelligent, secure, and scalable digital solutions by embedding AI as a core capability across the engineering landscape. We are seeking an experienced Machine Learning / AI Engineer to help design, build, and operationalize a central AI platform layer. This role focuses on enabling reusable AI services and capabilities such as AI SDKs, data masking, PII detection, and document intelligence. The engineer will also contribute to the reliable and compliant deployment of AI solutions through modern DevOps and MLOps practices.
Job Responsibility:
Designing and assessing serverless compute architectures for AI and data workloads
Building and optimizing scalable serverless data and machine learning environments
Developing and maintaining CI/CD pipelines for AI and data platform components
Ensuring reliability, performance, monitoring, and cost optimization of platform services
Collaborating with data scientists, cloud engineers, and DevOps teams as a technical advisor and subject matter expert
Supporting the operationalization of machine learning models in accordance with enterprise security, governance, and compliance standards
Requirements:
Proven experience designing and implementing machine learning and AI solutions in a cloud environment
Strong understanding of modern data and ML platform architectures, including serverless compute
Hands-on experience with serverless, integration, and event-driven architectures
Strong programming skills in Python, SQL, and distributed data processing frameworks
Experience with CI/CD, MLOps, and Infrastructure as Code practices
Solid understanding of scalability, cost management, and performance optimization for cloud workloads
Knowledge of security, privacy, and compliance considerations for AI and data solutions
Experience designing and implementing observability, monitoring, and alerting in production environments