AI-Powered SQL Agent (LangGraph)
Analysts and engineers spent excessive time writing and debugging SQL across complex schemas.
3 delivery phases - interactive demo
Read technical deep dive →Executive Portfolio
AI & Data Transformation Leader | Program Manager
13+ years delivering enterprise AI, analytics, and data platforms-driving digital transformation, responsible AI governance, and measurable business outcomes.
Five-minute path for interviewers: Summary, Experience, Projects, Impact, Contact.
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Years of Experience
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Team Members Led
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On-Time Delivery
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Programs & AI Initiatives
$0K+
Budget Managed
Experience
13+ years from engineering to program leadership - depth across data, AI, and delivery.
Present - Forward
Proof of work
Resume-backed builds with interactive demos - open any card to see how it was delivered.
Analysts and engineers spent excessive time writing and debugging SQL across complex schemas.
3 delivery phases - interactive demo
Read technical deep dive →SQL generation quality drifted without systematic user feedback incorporation.
3 delivery phases - interactive demo
Read technical deep dive →Device failures required near-real-time detection, not batch-only reporting.
3 delivery phases - interactive demo
Read technical deep dive →High-volume datasets exceeded legacy pipeline throughput.
3 delivery phases - interactive demo
Read technical deep dive →Sales teams lacked intelligent, contextual recommendations during outreach.
3 delivery phases - interactive demo
Read technical deep dive →Developers context-switched between IDE and pipeline tools to run SQLMesh commands.
3 delivery phases - interactive demo
Read technical deep dive →QA, testing, and LLM training needed realistic data without production exposure.
3 delivery phases - interactive demo
Read technical deep dive →In-memory limits blocked interactive analysis on large datasets.
3 delivery phases - interactive demo
Read technical deep dive →No objective TB-scale comparison across cloud environments for warehouse selection.
3 delivery phases - interactive demo
Read technical deep dive →Construction project stakeholders struggled to query Oracle Unifier without deep SQL skills.
3 delivery phases - interactive demo
Read technical deep dive →Technical Blog
Detailed write-ups on every built solution — architecture, tech stack, implementation, and lessons learned.
Built an AI SQL Agent using LangGraph and OpenAI models for automated query generation and workflow orchestration. Built with LangGraph, OpenAI, Python, SQL.
Designed a continuous improvement loop to capture feedback and retrain prompts/models for accuracy. Built with GenAI, Python, MLOps patterns.
Implemented streaming analytics with NATS, SQLMesh, and RisingWave for monitoring and failure detection. Built with NATS, SQLMesh, RisingWave, Python.
Outcomes
Measurable outcomes and flagship transformation programs - what changed for the business.
AI initiatives lacked interpretability, monitoring, and compliance controls, increasing operational risk.
30% reduction in operational AI risk; improved stakeholder confidence.
Unreliable data undermined analytics adoption and AI model performance.
Improved data reliability and compliance standards for downstream AI.
SQL authoring bottlenecks slowed analytics and increased error rates across teams.
Accelerated analytics delivery and improved SQL accuracy over time.
Batch-only monitoring delayed failure detection for connected devices.
Proactive operations and reduced downtime through real-time insights.
Warehouse selection and performance tuning lacked objective TB-scale evidence.
Defensible platform decisions and improved query economics at scale.
Finance stakeholders relied on fragmented reports with limited embedded insights.
15% improvement in decision efficiency; ~60% audit process automation.
Expertise
Where I operate across data, AI, architecture, and program delivery - select a pillar for detail.
End-to-end capability to modernize enterprises through architecture, data, AI, and disciplined delivery.
Yogendra Raghuvanshi is an AI & Data Transformation Leader | Program Manager based in Indore, India, with 13+ years delivering enterprise AI, analytics, and data platforms. He leads programs spanning Generative AI, SQLMesh pipelines, StarRocks benchmarking, Python automation, Power BI analytics, and responsible AI governance — with proven impact at Modern Data, Capgemini Invent, and GlobalLogic.
ACOS Optimization · AI Agents · Amazon Marketplace · Apache Spark · Bitbucket · CI/CD Concepts · Data Benchmarking · Data Engineering · Data Quality · Databricks · Decision Intelligence · Digital Transformation · Digital Twins · Documentation · Enterprise AI · Enterprise Analytics · ERD tooling · ETL Pipelines · GCP · GenAI · Generative AI · Git · Governance · Integration Architecture · Inventory Planning · JMeter · LangGraph · Legacy Monoliths · Metadata models · MinIO · ML · MLOps · MLOps patterns · MySQL · NATS · OCI · OpenAI · OpenAI / GenAI · Oracle SQL · Performance Engineering · Performance Testing · Platform Design · Polars · Power BI · Pricing Models · Process Automation · Product Analytics · Profitability Analysis · Program Management · Python · Query Optimization · React · Release Management · Reporting Systems · Responsible AI · RisingWave · Scalability · Semantic Layers · Snowflake · SQL · SQL / SQLMesh · SQLMesh · SSH · StarRocks · System Thinking · Terraform · TypeScript · Version Control · VS Code API
Connect
Reach out for executive roles, advisory engagements, or speaking opportunities.