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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates extensive expertise in architecting and implementing enterprise-grade GenAI solutions, with a strong focus on LLM applications, data engineering, and cloud platforms. Proven ability to lead technical teams and optimize performance across complex data pipelines.
Highest-signal resume keywords
15+ Years Experience in Data Engineering / Data Science / AI3+ Years Hands-On Experience in LLM / GenAI SolutionsStrong Python/Pyspark Engineering ExpertiseExperience with LangChain or Similar FrameworksTechnical Leadership in Prompt Engineering
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
Data EngineeringData ScienceLLM SolutionsArchitecture DesignAPI IntegrationData AnalysisETL/ELT PipelinesPrompt EngineeringGenAI ArchitecturesPerformance Optimization
Soft Skills
MentoringGuidance
Tools & Technologies
ClaudeOpenAILangChainLangGraphAzureAWSGCP
Industry Keywords
Enterprise DeliveryReference ArchitecturesReusable FrameworksRAG PipelinesData IngestionChunkingEmbeddingsVector SearchOrchestrationResponse Synthesis
Tech Stack
Tools & technologiesAWSAzureCloudETLGoogle Cloud PlatformPySparkPython
About the role
Key responsibilities & impact- Lead the design of enterprise-grade GenAI and agentic architectures (single-agent, multi-agent, tool-driven systems).
- Define reference architectures, reusable frameworks, and best practices for LLM applications across the organization.
- Architect and oversee implementation of end-to-end RAG pipelines: Data ingestion → chunking → embeddings → vector search → orchestration → response synthesis.
- Drive scalability, reliability, cost optimization, and performance across GenAI platforms.
- Provide technical leadership in prompt engineering, prompt orchestration, and agent workflows.
- Mentor and guide architects, engineers, and data scientists.
Requirements
What you’ll need- 15+ years of total experience in Data Engineering / Data Science / AI
- 3+ years of hands-on experience in LLM / GenAI solutions at scale
- Proven experience in architecture, solution design, and enterprise delivery
- Strong hands-on experience with LLMs (Claude, OpenAI, etc.)
- Experience with LangChain, LangGraph, or similar frameworks
- Strong Python/Pyspark engineering expertise with proven API integration experience
- Deep data analysis experience and handling large volume of data
- Prior experience in Data Engineering (ETL/ELT, pipelines, orchestration)
- Good exposure to Cloud platforms (Azure/AWS/GCP)
Benefits
Comp & perks- Health insurance
- Flexible work arrangements
- Professional development opportunities
