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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in leading the design and implementation of enterprise AI solutions, including GenAI and Agentic AI, while ensuring adherence to governance, compliance, and performance standards. Proven ability to manage cross-functional teams and translate business needs into scalable AI strategies and measurable outcomes.
Highest-signal resume keywords
AI Strategy DevelopmentEnterprise AI ArchitectureAzure AI Foundry ExperienceCross-Functional Team LeadershipProduction-Grade AI Solution Delivery
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
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Hard Skills
AI Solution Lifecycle ManagementGenAI ImplementationMLOps/LLMOpsData EngineeringConversational AI DevelopmentRAG ArchitectureKnowledge ManagementAI Evaluation and GovernancePerformance OptimizationScalability and Reliability
Soft Skills
Stakeholder ManagementStrategic ThinkingAnalytical Problem-SolvingCommunication SkillsExecutive Presentation Skills
Tools & Technologies
OpenAIDatabricksMicrosoft CopilotVector DatabasesEnterprise Search PlatformsWorkflow Automation ToolsAgent Orchestration FrameworksPrompt Engineering
Industry Keywords
Responsible AIAI GovernanceAI PlatformsAI-Powered Business ApplicationsEmerging AI Trends
Tech Stack
Tools & technologiesAzureCloud
About the role
Key responsibilities & impact- Lead the design, development, delivery, governance, and scaling of enterprise AI, GenAI, Agentic AI, and RAG-based solutions
- Define AI strategy, roadmaps, governance frameworks, operating models, architecture standards, and best practices
- Drive architecture and implementation of AI platforms, copilots, multi-agent systems, conversational AI, and intelligent automation initiatives
- Identify high-value AI use cases with business stakeholders, establish success metrics, and realize business value
- Oversee the full AI solution lifecycle from ideation and architecture through development, deployment, monitoring, continuous improvement, and adoption
- Lead and mentor cross-functional teams of AI engineers, data scientists, architects, developers, and delivery teams
- Establish Responsible AI, security, privacy, compliance, safety, risk management, quality, and performance standards
- Drive adoption using Azure AI Foundry, OpenAI, Databricks, vector databases, enterprise search, and modern AI frameworks
- Guide RAG pipelines, knowledge management solutions, agentic workflows, and AI-powered business applications
- Manage stakeholder communications, executive reporting, budgeting, resource planning, and delivery governance
- Define evaluation frameworks, quality standards, performance metrics, and operational readiness
- Contribute to capability building, reusable accelerators, reference architectures, and AI practice thought leadership
- Stay current with emerging GenAI, Agentic AI, multimodal AI, AI agent, and enterprise AI platform trends
Requirements
What you’ll need- BTech/Masters/PhD educational qualification
- Bachelor’s/master’s degree in computer science, Data Science, Engineering, or related field
- 10+ years of overall experience
- 5+ years leading AI/ML, GenAI, or data-driven transformation initiatives in enterprise environments
- Hands-on enterprise or production experience delivering AI solutions; certifications, personal projects, and demos alone are insufficient
- Ability to explain at least two AI/GenAI implementations, including use case, contribution, technical approach, and measurable outcomes
- Experience delivering and scaling production-grade AI, GenAI, Agentic AI, Copilot, and RAG-based solutions
- Strong understanding of enterprise AI architecture
- Experience with LLM and GenAI application development, RAG architectures, knowledge management, Agentic AI, multi-agent systems, Conversational AI, Copilot solutions, AI evaluation, governance, Responsible AI, AI platform engineering, cloud deployments, MLOps/LLMOps, data engineering, vector databases, and retrieval pipelines
- Ability to lead cross-functional teams of AI engineers, architects, data scientists, and developers
- Strong stakeholder management and ability to translate business needs into scalable AI solutions and measurable outcomes
- Experience with Azure AI Foundry, OpenAI, Databricks, Microsoft Copilot, and related AI ecosystems
- Knowledge of Azure OpenAI, vector databases, enterprise search platforms, prompt engineering, agent orchestration frameworks, tool integrations, workflow automation, observability, guardrails, model monitoring, performance optimization, enterprise architecture, software engineering, scalability, reliability, and secure solution design
- Strong analytical, problem-solving, strategic thinking, communication, stakeholder management, and executive presentation skills
Benefits
Comp & perks- Competitive salary
- Continuous learning opportunities
- Tools and flexibility to make a meaningful impact
- Insights, coaching, and leadership development
- Diverse and inclusive culture
- Collaboration with global clients, AI experts, analytics leaders, and industry specialists
- Exposure to learning, skills, and insights across EY’s global delivery network