Experience Range: with at least 6 years of experience in AI/ML engineering, including hands-on expertise in designing, building, and operationalizing enterprise-scale AI platforms Key Responsibilities:
Lead the design and architecture of enterprise-scale AI platforms, ensuring scalability, security, and operational readinessDefine and implement frameworks for model lifecycle management, MLOps/LLMOps, and AI observability to support robust deployment and monitoringEstablish and enforce Responsible AI principles, governance frameworks, and technical guardrails across AI solutionsDrive platform-level technical decision-making and define reusable architecture patterns, standards, and reference models for AI and GenAI solutionsCollaborate with business, data, technology, and platform teams to translate AI architecture principles into production-ready capabilitiesEvaluate emerging AI technologies and assess their applicability to enterprise AI platforms, supporting long-term scalability and business outcomesMaintain and improve AI model governance, version control, and documentation for ongoing operational excellenceRequired Skills:
Expertise in enterprise-scale AI platform design and operationalizationDeep proficiency in model lifecycle management, MLOps/LLMOps, and AI observabilityAdvanced programming skills in Python and PySparkExperience with cloud-based AI platforms and modern data/AI architecturesKnowledge of Responsible AI, AI governance, model risk, security, and complianceHands-on experience with KubeFlow and BentoML for ML pipeline orchestrationCompetence in classification algorithms such as decision trees and SVMExperience with Great Expectations and Evidently AI for model validationStrong proficiency in regression analysis (linear and logistic)Statistical analysis and computing for large datasetsPreferred Skills:
Experience with GenAI architectures, large language models, and AI agentsProficiency in advanced ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, or MXNetKnowledge of vector databases and AI orchestrationUnderstanding of AI observability and Responsible AI frameworksExperience developing reusable AI architecture patterns and acceleratorsDesired Qualifications:
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related disciplineCertification in Machine Learning or Data Science (e.g., TensorFlow Developer Certificate, Microsoft Certified: Azure AI Engineer Associate)Relevant certification in statistical analysis or advanced analytics (e.g., SAS Certified Specialist, IBM Data Science Professional Certificate)