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Vaishnavi Veerkumar
Mid-level AI Engineer specializing in GenAI and RAG systems
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About
AI engineer who built a production e-commerce system that analyzes product images alongside sales and demographic data to generate actionable creative recommendations, now used by 20+ clients. Also built orchestrated document/agent pipelines (Airflow, LangGraph) including a compliance drift detector auditing 401 compliance documents, with an emphasis on traceability, logging, and production integration.
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Key Strengths
- Built and deployed an AI-driven e-commerce image recommendation system from scratch serving 20+ clients
- Strong at diagnosing data issues (duplicate reference images) that caused generic model outputs and fixing via dataset cleanup
- Designed exclusion/filtering model to remove low-signal inputs (text-heavy images) to improve recommendation quality
- Hands-on orchestration experience (Airflow, LangGraph) for multi-stage pipelines and agent workflows
- Production reliability mindset: traceability from retrieval → trends extracted → prompt → output via robust logging
- Able to translate non-technical stakeholder needs into solvable AI problems and communicate outputs clearly
Reference Highlights
- Strong end-to-end ownership from research to production implementation
- Excellent cross-team collaboration
- Fantastic documentation
- Comes to meetings well-prepared with resources (links, docs)
- Proactive communication (asks for more use cases to test)
- Patient and clear with non-technical stakeholders
- Able to explain LLMs and prompting at a basic level
- Organized communicator
- Uses diagrams to clarify complex processes
- Well-paced verbal communication and invites questions
- Great attitude
- Effective with early adopters/customers
- Owned a computer-vision generative AI product end-to-end within the platform
- Strong at shipping AI products into production
- Strategic, data-driven approach to ambiguous problems
- Effectively integrates market insights, policy constraints, and technical experimentation
- Ensures output quality and diversity (not derivative results)
- Handled R&D to engineering/production transfer smoothly
- Proactive, constant communication across teams
- Creates validation sets to support engineering development and debugging
- Thorough preparation with many concrete examples for discussions
- Translates technical tradeoffs into business/strategy guidance for product managers
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