Nikita Verma

Built multilingual (English + Spanish) real-time ASR/STT systems using NVIDIA Parakeet-TDT-0.6B-v3, Parakeet-CTC-0.6B-ES, Parakeet-RNNT-1.1B, Nemotron Speech Streaming, Whisper Large-v3 Turbo, Google STT v2, and Azure Speech Services supporting live microphone and audio file streaming with partial and final transcript generation. Architected WebSocket ASR servers with FastAPI + Uvicorn featuring chunk-wise PCM streaming, silence-based finalization, manual flush triggers, and JSON partial/final transcript events; tuned VAD systems (WebRTC, Adaptive Energy, Silero) with silence thresholds and chunk durations for near real-time delivery. Conducted structured ASR model benchmarking across Parakeet-TDT/CTC-ES/RNNT, Riva, Whisper, Google STT, and Azure STT evaluating TTFT, TTFB, WER, latency, context-switching score, and transcript quality; generated detailed Excel-based evaluation reports. Containerized GPU-accelerated ASR inference services using Docker, CUDA 12.x, NIM, and optimized Dockerfiles; resolved critical issues for high-availability production deployment. Deployed and operated real-time ASR services on GCP and AWS EC2, managing VM provisioning, Docker registry pushes, port/firewall rules, SSH tunneling, and remote endpoint validation. Evaluated NVIDIA Triton Inference Server, NIM, Riva, and vLLM for speech AI model serving and deployment strategies.

Designed, built, and maintained ML/statistical models to optimize marketing performance across audience targeting, channels, campaigns, and customer journeys. Specialized in Marketing Mix Modeling (MMM), LTV prediction, lift estimation, and causal inference. Designed and deployed a financial IVA chatbot in Python using Amazon Lex, Lambda, and Bedrock to support real-time user queries with ongoing integration of Amazon Polly and Connect for voice support. Performed large-scale data processing and exploratory analysis using Python, SQL, and Spark to uncover actionable insights and evaluate marketing performance metrics.

Orchestrated AI/ML/LLM models, improving automation by 30% using NLP and SciPy, and leveraging image recognition for enhanced insights. Developed and deployed ASR and TTS machine learning models using PyTorch, TensorFlow, and NumPy for real-time translation, integrated Face and OCR models for automated identification and data extraction, and enhanced customer satisfaction by 20% through AI solution integration. Built a traditional RASA-based conversational chatbot and later integrated LLaMA and RAG to transform it into an intelligent virtual assistant for real-time interactions. Deployed, customized, and fine-tuned GPT-3 and GPT-4 on proprietary datasets, elevating model response accuracy and efficiency by over 40%. Specialized in supervised learning and classification algorithms, enhancing model accuracy to 90% with dynamic retraining, data augmentation, and hyperparameter tuning. Managed MySQL database systems and designed real-time data pipelines using Apache Kafka and Pyspark. Skilled in coding with Python, Scala, and Shell Scripting, and proficient in project management using Git/GitLab.