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Senior AI/ML Engineer - R01572485

Brillio-2
Bengaluru, IND
Senior · 6+ years experience
Discovered 2 weeks ago
azurekeraskubeflowpythonpytorchsas

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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
  • 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
  • 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
  • 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

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
  • 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)

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