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Ainesh Gunturu
Junior Data Analyst specializing in marketing analytics and machine learning
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About
Built and deployed a production LLM-assisted recommendation and insights platform that unifies structured, semi-structured, and unstructured data via a modular ingestion pipeline, canonical schemas, embeddings, and late-fusion modeling. Experienced in operationalizing ML/LLM systems with Airflow and Kubernetes (Dockerized services, autoscaling, rolling updates) and emphasizes reliability through layered testing, guardrails, monitoring, and A/B experimentation while partnering closely with non-technical stakeholders.
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Key Strengths
- Built and deployed production LLM-assisted recommendation/insights system over heterogeneous data
- Architected modular ingestion with canonical schema normalization for mixed structured/unstructured inputs
- Applied embeddings + late fusion to combine signals across data types
- Integrated ML/LLM capabilities into existing production workflows using sidecar pattern
- Designed for real-time performance with latency optimization
- Strong MLOps/orchestration: Airflow DAGs with sensors/backfills and Kubernetes-based scalable serving
- Reliability-focused agent/workflow engineering with layered testing, guardrails, monitoring, and A/B testing
- Effective collaboration with non-technical stakeholders via requirements translation and iterative prototyping
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