Senior AI/ML Engineer - R01572485

Senior AI/ML Engineer Job requirements 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 readiness - Define and implement frameworks for model lifecycle management, MLOps/LLMOps, and AI observability to support robust deployment and monitoring - Establish and enforce Responsible AI principles, governance frameworks, and technical guardrails across AI solutions - Drive platform-level technical decision-making and define reusable architecture patterns, standards, and reference models for AI and GenAI solutions - Collaborate with business, data, technology, and platform teams to translate AI architecture principles into production-ready capabilities - Evaluate emerging AI technologies and assess their applicability to enterprise AI platforms, supporting long-term scalability and business outcomes - Maintain and improve AI model governance, version control, and documentation for ongoing operational excellence Required Skills: - Expertise in enterprise-scale AI platform design and operationalization - Deep proficiency in model lifecycle management, MLOps/LLMOps, and AI observability - Advanced programming skills in Python and PySpark - Experience with cloud-based AI platforms and modern data/AI architectures - Knowledge of Responsible AI, AI governance, model risk, security, and compliance - Hands-on experience with KubeFlow and BentoML for ML pipeline orchestration - Competence in classification algorithms such as decision trees and SVM - Experience with Great Expectations and Evidently AI for model validation - Strong proficiency in regression analysis (linear and logistic) - Statistical analysis and computing for large datasets Preferred Skills: - Experience with GenAI architectures, large language models, and AI agents - Proficiency in advanced ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, or MXNet - Knowledge of vector databases and AI orchestration - Understanding of AI observability and Responsible AI frameworks - Experience developing reusable AI architecture patterns and accelerators Desired Qualifications: - Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline - Certification 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)