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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, Agentic AI, and RAG architectures, while ensuring compliance with Responsible AI practices. Proven ability to manage cross-functional teams and drive business value through strategic AI initiatives and stakeholder collaboration.
Highest-signal resume keywords
AI Solution DeliveryEnterprise AI ArchitectureCross-Functional Team LeadershipStakeholder ManagementMLOps/LLMOps
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
AI/ML ImplementationGenAI Application DevelopmentRAG ArchitectureConversational AIAI GovernanceData EngineeringVector DatabasesAI Platform EngineeringOperationalization of AI SolutionsPrompt Engineering
Soft Skills
Analytical SkillsProblem-SolvingStrategic ThinkingCommunicationExecutive Presentation Skills
Tools & Technologies
Azure AI FoundryOpenAIDatabricksMicrosoft CopilotAI Ecosystems
Certifications & Qualifications
BTechMastersPhD in Computer ScienceData ScienceEngineering
Industry Keywords
Responsible AISecurityPrivacyComplianceRisk ManagementEnterprise ArchitectureSoftware EngineeringScalabilityReliabilityWorkflow Automation
Tech Stack
Tools & technologiesAzureCloud
About the role
Key responsibilities & impact- Lead the design, development, and delivery 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
- Collaborate with business stakeholders to identify high-value use cases, establish success metrics, and realize business value
- Oversee the full AI solution lifecycle from ideation and architecture through development, deployment, monitoring, adoption, and continuous improvement
- Lead and mentor cross-functional teams of AI engineers, data scientists, architects, and developers
- Establish Responsible AI, security, privacy, compliance, safety, and risk-management practices
- Drive adoption of enterprise AI capabilities using Azure AI Foundry, OpenAI, Databricks, vector databases, and modern AI frameworks
- Define evaluation frameworks, quality standards, performance metrics, guardrails, and observability practices
- Guide RAG architectures, knowledge management solutions, and agentic workflows
- Manage stakeholder communication, executive reporting, budgeting, resource planning, and delivery governance
- Stay current with emerging GenAI, Agentic AI, multimodal AI, AI agent, and enterprise AI platform trends
- Contribute to capability building, reusable accelerators, reference architectures, and AI thought leadership
- Partner with business and technology stakeholders to prioritize use cases and deliver measurable business value
Requirements
What you’ll need- 10+ years of overall experience
- 5+ years leading AI/ML, GenAI, or data-driven transformation initiatives in enterprise environments
- Hands-on experience delivering AI solutions in enterprise or production environments
- Ability to explain at least two AI/GenAI implementations, including business use case, contribution, technical approach, and measurable outcomes
- Proven experience delivering and scaling production-grade AI, GenAI, Agentic AI, Copilot, and RAG-based solutions from concept through deployment and adoption
- Strong understanding of enterprise AI architecture
- Experience with LLM and GenAI application development
- Experience with RAG architectures and knowledge management solutions
- Experience with Agentic AI and multi-agent systems
- Experience with Conversational AI and Copilot solutions
- Experience with AI evaluation, governance, and Responsible AI
- Experience with AI platform engineering and cloud-based deployments
- Experience with MLOps/LLMOps and operationalization of AI solutions
- Experience with data engineering, vector databases, and retrieval pipelines
- Demonstrated ability to lead cross-functional teams
- Strong stakeholder management skills
- Experience with Azure AI Foundry, OpenAI, Databricks, Microsoft Copilot, and related AI ecosystems
- BTech/Masters/PhD educational qualification
- Bachelor’s/master’s degree in computer science, Data Science, Engineering, or related field
- Strong knowledge of Azure OpenAI, Databricks, vector databases, enterprise search platforms, prompt engineering, agent orchestration frameworks, tool integrations, workflow automation, AI governance, security, privacy, compliance, safety, risk management, 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- Continuous learning and development opportunities
- Tools and flexibility to make a meaningful impact
- Insights, coaching and confidence-building leadership development
- Diverse and inclusive culture
- Opportunity to work with global clients and well-known brands
- Collaboration with AI experts, analytics leaders, and industry specialists
- Exposure to projects across multiple client sectors