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Keerthana Tammina
Mid-level Data Scientist specializing in machine learning and generative AI
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About
ML/LLM engineer who has shipped a production transformer-based document understanding system on AWS, owning the full pipeline from domain fine-tuning to Dockerized CI/CD deployment. Demonstrates strong production rigor—latency optimization (distillation/quantization, async batching, autoscaling), orchestration with Airflow/Step Functions/Azure Data Factory, and monitoring/drift detection—plus experience translating ops stakeholder needs into adopted AI automation via dashboards.
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Key Strengths
- Built and deployed production NLP document-understanding system (extraction, classification, structured outputs)
- End-to-end ML delivery: fine-tuning on domain data through AWS deployment with Docker and CI/CD
- Inference-latency reduction via distillation, quantization, preprocessing optimization, async batching, and autoscaling
- Orchestrated reliable ML/data workflows using Airflow (dependencies, retries/backoff, alerts) with CloudWatch monitoring
- Reliability-focused evaluation approach: unit tests, synthetic/edge-case simulations, continuous monitoring, drift detection, retraining
- Pragmatic model/retrieval/prompt selection driven by empirical testing and latency/accuracy constraints
- Effective collaboration with non-technical stakeholders; delivered operational automation with dashboard-based adoption
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