Sai Teja S.

• Designed and deployed production-grade NLP and GenAI solutions using transformer-based models (BERT/GPT) for text classification, sentiment analysis, and entity extraction. • Built semantic search and RAG pipelines using embeddings and vector similarity to improve information retrieval accuracy and reduce manual effort. • Developed LLM-powered applications with prompt engineering, retrieval orchestration, and guardrails to minimize hallucinations. • Deployed ML/NLP models as RESTful APIs using Docker and Kubernetes, integrated with enterprise systems. • Implemented MLOps pipelines (CI/CD, model monitoring, versioning) to support reliable production deployments. • Collaborated with product, engineering, and business teams to translate requirements into scalable AI solutions.

• Developed and optimized NLP pipelines using NLTK and transformer models for document classification, topic modeling, and text analytics. • Built semantic similarity and search solutions leveraging embeddings for large enterprise datasets. • Designed distributed NLP workflows using Spark to process high-volume unstructured text data. • Applied model optimization techniques including performance tuning and inference efficiency improvements. • Partnered with cross-functional teams to deliver scalable, production-ready ML solutions.

• Built and deployed NLP and ML models for customer analytics, sentiment analysis, and intent detection. • Exposed models via RESTful APIs to support real-time inference at scale. • Implemented feature engineering and text preprocessing pipelines for large unstructured datasets. • Monitored model performance and supported continuous improvements in production environments.

• Designed end-to-end AI/ML and NLP solutions across multiple client engagements. • Deployed solutions on cloud platforms (AWS/GCP/Azure) with CI/CD and containerized workflows. • Worked closely with stakeholders to translate business problems into analytical and ML-driven solutions.