Hire Aravind B. - Vector Database Engineer

* Architected and owned end-to-end GenAI recommendation pipelines, covering data ingestion, feature * engineering, model training, evaluation, deployment, and monitoring. * Designed and implemented LLM-powered personalization systems using Hugging Face Transformers, * LangChain, FAISS, and Pinecone to dynamically generate and rank content. * Built Retrieval-Augmented Generation (RAG) architectures integrating vector search, embeddings, and real-time * user context for high-relevance recommendations. * Led model fine-tuning and experimentation using PyTorch and TensorFlow, including hyperparameter * optimization, offline evaluation, and A/B testing. * Developed scalable inference services using FastAPI and Flask, enabling low-latency, high-throughput model * serving. * Containerized and deployed AI services using Docker and Kubernetes, implementing autoscaling and rollout * strategies. * Implemented model monitoring and performance tracking, including latency, drift detection, and quality metrics. * Collaborated cross-functionally with product managers, data engineers, and platform teams to translate business * requirements into production-ready ML solutions. * Contributed to MLOps best practices, including CI/CD pipelines, MLflow experiment tracking, and reproducible * training workflows. * Ensured data quality, governance, and security compliance across AI pipelines.

* Designed and owned large-scale recommendation systems using collaborative filtering, deep learning, and * ranking models. * Built end-to-end ML pipelines for personalization, demand forecasting, and customer behavior modeling. * Performed feature engineering, data validation, and statistical analysis on high-volume datasets. * Developed and productionized models on AWS SageMaker, Lambda, and EC2, supporting real-time and batch * inference. * Implemented RESTful ML services using FastAPI and Flask to integrate models into downstream applications. * Developed NLP pipelines for text classification, sentiment analysis, and named entity recognition using BERT, * spaCy, and NLTK. * Built computer vision models using TensorFlow and OpenCV for automated defect detection and image feature * extraction. * Optimized ETL workflows and data pipelines using Airflow, Spark, Snowflake, and Redshift. * Implemented model monitoring, retraining strategies, and performance dashboards. * Partnered with software engineers and business stakeholders to align ML outputs with business KPIs