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)
Summary
Lead design and architecture of enterprise-scale AI platforms, defining MLOps/LLMOps, model lifecycle, observability, and Responsible AI governance for production deployment. Requires at least 6 years in AI/ML engineering with Python, PySpark, Kubeflow, BentoML, and cloud AI platforms, plus a bachelor's in Computer Science, Data Science, Statistics, or Mathematics.
Responsibilities
Lead design and architecture of enterprise-scale AI platforms; Define frameworks for model lifecycle management, MLOps/LLMOps, and AI observability; Establish Responsible AI principles, governance and guardrails; Define reusable architecture patterns and standards; Collaborate with business, data and technology teams; Evaluate emerging AI technologies
Qualifications
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics or closely related discipline; Certification in Machine Learning or Data Science; Certification in statistical analysis or advanced analytics
Education requirements
Bachelor's degree in Computer Science, Data Science, Statistics, Mathematics, or a closely related discipline
Experience requirements
at least 6 years of experience in AI/ML engineering, including hands-on expertise in designing, building, and operationalizing enterprise-scale AI platforms