Senior AI/ML Engineer \n 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) \n