Pradeep . — AI Researcher

I am an AI/ML Software Engineer with 5+ years of hands-on experience building and operating production-grade machine learning systems across retail, financial services, and data platforms. My work focuses on applied machine learning, distributed systems, and MLOps, with end-to-end ownership from data ingestion and feature engineering to production deployment, monitoring, and continuous optimization. I have built scalable AI systems using Python, SQL, PySpark, NumPy, Pandas, PyTorch, TensorFlow, Hugging Face Transformers, and scikit-learn, working across computer vision, NLP, semantic search, and Retrieval-Augmented Generation (RAG). I approach ML as a systems engineering problem, balancing model accuracy with latency, throughput, reliability, and cost in real-world environments. Currently at Sam’s Club, I work on real-time computer vision platforms using NVIDIA DeepStream and OpenCV, processing 8–12M video frames per day with sub-120 ms end-to-end latency. I designed event-driven pipelines using RabbitMQ and Kafka, sustaining 15K+ events/sec with 99.95% delivery reliability. I also built embedding-based retrieval services using Hugging Face models and Milvus, enabling low-latency similarity search across 50M+ vectors. Performance optimizations in Redis-based caching and feature lookups reduced retrieval latency by 35–50%. Previously at Wells Fargo, I built a production-grade RAG system using GPT-4, Claude, LLaMA, LangChain, Hugging Face embeddings, and Elasticsearch, reducing fraud investigation time by 45–60%. I developed NLP pipelines with TensorFlow and spaCy processing 1.0–1.4M documents/day at 90–93% accuracy, and implemented semantic search using PySpark and FAISS with sub-280 ms query latency across 18M+ records. Streaming pipelines using Kafka and Spark handled 250–400 GB/day of unstructured data. Across roles, I have deployed ML services using Docker, Kubernetes, Triton Inference Server, MLflow, AWS, and Azure, with monitoring via Prometheus, Grafana, and Tableau. I collaborate closely with product, platform, and data teams, enjoy translating ambiguous problems into scalable systems, and care deeply about building AI solutions that are robust, maintainable, and trusted in production.

Stackforce AI infers this person is a Machine Learning Engineer with expertise in Retail and Fintech sectors.

Experience: 2 yrs 10 mos