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EY

SAP BTP Applied Intelligence AIML Manager

EY

AI and machine learning leader scaling enterprise AI engineering at EY. Architecting predictive, generative, and autonomous solutions while building high-performing technical teams.

Posted 8/17/2026full-timeNoida • 🇮🇳 IndiaMid-LevelSeniorWebsite

Core Competencies

Role fit
Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in AI solution engineering, including machine learning, deep learning, and MLOps practices, while effectively leading and mentoring technical teams. Capable of translating complex AI concepts into business value and driving innovation in cloud-based AI services.

Highest-signal resume keywords
Machine LearningDeep LearningMLOpsPythonAI Solution Engineering

ATS Keywords

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Hard Skills
Statistical ModellingModel TrainingModel OptimisationModel EvaluationGenerative AIFeature EngineeringAI GovernancePredictive AnalyticsComputer VisionNatural Language Processing
Soft Skills
Client ManagementCommunicationPresentationStakeholder ManagementCoaching
Tools & Technologies
TensorFlowPyTorchScikit-learnLangChainLangGraphSAP BTPAzureAWSGoogle CloudAI/ML
Certifications & Qualifications
MLOps CertificationData Science Certification
Industry Keywords
AI EngineeringAI Risk ManagementResponsible AIKnowledge GraphsMulti-Agent Systems

Tech Stack

Tools & technologies
AWSAzureCloudPythonPyTorchScikit-LearnTensorflow

About the role

Key responsibilities & impact
  • Lead the development and growth of advanced AI engineering capabilities across machine learning, deep learning, MLOps, and LLMOps disciplines on SAP BTP and other cloud platforms
  • Define technical standards, engineering frameworks, and best practices for enterprise AI solution development
  • Architect and oversee AI solutions spanning predictive analytics, computer vision, NLP, recommendation systems, intelligent automation, and Generative AI
  • Establish scalable model development, deployment, monitoring, and governance processes
  • Drive adoption of MLOps and LLMOps practices to improve reliability, traceability, observability, and operational performance
  • Mentor and develop AI engineers beyond prompt engineering into broader AI engineering disciplines
  • Collaborate with business and technology stakeholders to identify high-value AI opportunities and convert them into production solutions
  • Evaluate emerging AI frameworks, tooling, and platforms
  • Lead technical reviews, solution assurance activities, architecture discussions, and capability-building initiatives
  • Support thought leadership, innovation activities, technical asset development, and market-facing AI initiatives

Requirements

What you’ll need
  • Strong experience in machine learning, deep learning, statistical modelling, and AI solution engineering
  • Hands-on expertise with Python and modern AI ecosystems including TensorFlow, PyTorch, Scikit-learn, LangChain, and LangGraph or comparable frameworks
  • Deep understanding of model training, optimisation, evaluation, deployment, monitoring, and lifecycle management
  • Experience establishing MLOps, LLMOps, data science engineering, and AI platform practices
  • Knowledge of modern data engineering, feature engineering, model observability, and AI governance
  • Excellent client management experience across multiple and varied demographical contexts
  • Practical experience with Generative AI, foundation models, RAG architectures, AI agents, and orchestration frameworks
  • Strong understanding of cloud-based AI services and distributed AI workloads
  • Ability to balance research-oriented innovation with enterprise delivery requirements
  • Excellent communication, presentation, stakeholder management, and consulting skills
  • Demonstrated experience leading technical teams and coaching engineers
  • Experience building and managing AI engineering teams or centres of excellence
  • Exposure to large-scale AI programmes across multiple industries
  • Experience with model evaluation frameworks, AI risk management, Responsible AI, and AI governance
  • Familiarity with knowledge graphs, multi-agent systems, reinforcement learning, or advanced deep learning techniques
  • SAP BTP, Azure, AWS, Google Cloud, AI/ML, Data Science, or MLOps certifications
  • Contributions to AI communities, publications, open-source projects, patents, or recognised thought leadership
  • Technically credible AI leader with deep foundations in machine learning and modern AI engineering
  • Ability to create capabilities, frameworks, teams, and engineering practices
  • Ability to translate complex AI concepts into business value
  • Ability to develop AI engineers and technical leaders
  • Ability to take ownership and drive outcomes with limited direction
  • Ability to collaborate in fast-paced, multidisciplinary environments and partner with senior stakeholders

Benefits

Comp & perks
  • Flexible environment
  • Diverse and inclusive culture
  • Professional development opportunities
  • Opportunities to build new skills and take on leadership roles
  • Mentorship and opportunities to connect and grow
  • Health and wellness packages
  • Rewards
  • Learning opportunities
  • Disability-related adjustments or accommodations during recruitment