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Lead Data Scientist

RapidCue AI

Mohadi Mokasa · On-site Full-time Lead Today

About the role

About

RapidCue AI is an innovative platform built as a real-time AI sales copilot and autopilot, designed to enable smarter conversations and faster conversions in sales and customer support calls. It provides live support for sales representatives during calls and can also autonomously handle interactions when needed. By combining voice AI, large language models, and real-time intelligence, RapidCue AI helps teams improve speed, consistency, and performance at scale.

We are building at the intersection of Generative AI, speech intelligence, and agentic automation to redefine how modern sales teams operate.

Role Description

This is a full-time, on-site role for a Data Scientist, based in Bengaluru. In this role, you will work on the development of advanced AI systems powering real-time sales intelligence, conversational automation, and decision support across voice-based workflows.

You will contribute to building and improving Generative AI and machine learning solutions, including LLM-powered agents, speech-to-text pipelines, conversational intelligence, prompt orchestration, retrieval-augmented generation (RAG), and real-time recommendation systems. You will collaborate closely with product, engineering, and business teams to build scalable AI-first products that improve sales effectiveness and customer engagement.

Your day-to-day responsibilities will include developing and testing production-ready AI models, designing agentic workflows, improving voice and language understanding systems, analyzing large datasets, and helping translate AI capabilities into measurable business impact.

Key Responsibilities

  • Build and improve GenAI-driven products for real-time sales assistance and autonomous voice interactions
  • Develop systems involving LLMs, LangChain/LangGraph-style orchestration, agentic workflows, RAG pipelines, and prompt engineering
  • Work on speech-to-text, text-to-speech, speaker intelligence, call summarization, intent detection, and real-time conversation analysis
  • Build AI pipelines for live call assistance, next-best response generation, objection handling, lead qualification, and conversational automation
  • Design and deploy ML, NLP, and voice AI models for production use cases
  • Work with structured and unstructured datasets including call transcripts, CRM data, conversation logs, and sales outcomes
  • Collaborate with cross-functional teams to align AI capabilities with product and business goals
  • Run experiments, evaluate model performance, and continuously improve AI systems
  • Contribute to scalable deployment practices with focus on latency, reliability, and real-time performance
  • Stay updated with the latest advancements in LLMs, multimodal AI, speech AI, and agentic systems

Preferred Background / Experience

  • Strong experience in Generative AI, Large Language Models (LLMs), and applied NLP
  • Hands-on experience with frameworks such as LangChain, LangGraph, LlamaIndex, or similar orchestration frameworks
  • Experience building agentic AI systems involving reasoning, memory, tools, and workflow automation
  • Good understanding of speech-to-text (ASR), voice intelligence, transcription systems, and conversational AI
  • Familiarity with real-time audio/voice pipelines for sales, support, or contact-center use cases
  • Experience with RAG architectures, vector databases, embeddings, semantic search, and prompt optimization
  • Strong knowledge of machine learning, deep learning, predictive modeling, and statistical analysis
  • Experience deploying AI/ML models into production with attention to latency, scalability, and monitoring
  • Exposure to MLOps / LLMOps, model evaluation, experimentation, and data pipelines
  • Ability to solve business problems through practical AI systems in fast-moving environments

Qualifications

  • Strong expertise in Data Science, Machine Learning, NLP, and applied statistics
  • Proven experience working with LLMs, GenAI applications, and conversational AI systems
  • Solid understanding of statistical modeling, predictive analytics, and experimentation
  • Experience working with large and complex datasets, especially text, audio, or conversational data
  • Strong analytical and problem-solving ability
  • Good communication and collaboration skills across teams
  • Bachelor’s / Master’s degree in Data Science, Computer Science, Statistics, AI, Machine Learning, or a related field preferred

Requirements

  • Strong expertise in Data Science, Machine Learning, NLP, and applied statistics
  • Proven experience working with LLMs, GenAI applications, and conversational AI systems
  • Solid understanding of statistical modeling, predictive analytics, and experimentation
  • Experience working with large and complex datasets, especially text, audio, or conversational data
  • Strong analytical and problem-solving ability
  • Good communication and collaboration skills across teams

Responsibilities

  • Build and improve GenAI-driven products for real-time sales assistance and autonomous voice interactions
  • Develop systems involving LLMs, LangChain/LangGraph-style orchestration, agentic workflows, RAG pipelines, and prompt engineering
  • Work on speech-to-text, text-to-speech, speaker intelligence, call summarization, intent detection, and real-time conversation analysis
  • Build AI pipelines for live call assistance, next-best response generation, objection handling, lead qualification, and conversational automation
  • Design and deploy ML, NLP, and voice AI models for production use cases
  • Work with structured and unstructured datasets including call transcripts, CRM data, conversation logs, and sales outcomes
  • Collaborate with cross-functional teams to align AI capabilities with product and business goals
  • Run experiments, evaluate model performance, and continuously improve AI systems
  • Contribute to scalable deployment practices with focus on latency, reliability, and real-time performance
  • Stay updated with the latest advancements in LLMs, multimodal AI, speech AI, and agentic systems

Skills

AIASRConversational AIData ScienceDeep LearningEmbeddingsGenerative AILangChainLangGraphLlamaIndexLLMLLMOpsMachine LearningMLOpsMultimodal AINLPPredictive ModelingPrompt EngineeringRAGSpeech AISpeech-to-TextText-to-SpeechVector DatabasesVoice AI

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