Job Title

About the RoleWe are looking for an AI Engineer with experience in Speech-to-text and Text Generation to solve a Conversational AI challenge for our client based in EMEA. The focus of this project is to transcribe conversations and leverage generative AI-powered text analytics to drive better engagement strategies and decision-making.The ideal candidate will have deep expertise in Speech-to-Text (STT), Natural Language Processing (NLP), Large Language Models (LLMs), and Conversational AI systems. This role involves working on real-time transcription, intent analysis, sentiment analysis, summarization, and decision-support tools.Key Responsibilities1. Conversational AI & Call Transcription DevelopmentDevelop and fine-tune automatic speech recognition (ASR) modelsImplement language model fine-tuning for industry-specific language.Develop speaker diarization techniques to distinguish speakers in multi-speaker conversations.2. NLP & Generative AI ApplicationsBuild summarization models to extract key insights from conversations.Implement Named Entity Recognition (NER) to identify key topics.Apply LLMs for conversation analytics and context-aware recommendations.Design custom RAG (Retrieval-Augmented Generation) pipelines to enrich call summaries with external knowledge.3. Sentiment Analysis & Decision SupportDevelop sentiment and intent classification models.Create predictive models that suggest next-best actions based on call content, engagement levels, and historical data.4. AI Deployment & ScalabilityDeploy AI models using tools like AWS, GCP, Azure AI, ensuring scalability and real-time processing.Optimize inference pipelines using ONNX, TensorRT, or Triton for cost-effective model serving.Implement MLOps workflows to continuously improve model performance with new call data.QualificationsTechnical Skills Strong experience in Speech-to-Text (ASR), NLP, and Conversational AI.Hands-on expertise with tools like Whisper, DeepSpeech, Kaldi, AWS Transcribe, Google Speech-to-Text.Proficiency in Python, PyTorch, TensorFlow, Hugging Face Transformers.Experience with LLM fine-tuning, RAG-based architectures, and LangChain.Hands-on experience with Vector Databases (FAISS, Pinecone, Weaviate, ChromaDB) for knowledge retrieval.Experience deploying AI models using Docker, Kubernetes, FastAPI, Flask.Soft Skills Ability to translate AI insights into business impact.Strong problem-solving skills and ability to work in a fast-paced AI-first environment.Excellent communication skills to collaborate with cross-functional teams, including data scientists, engineers, and client stakeholders.Preferred Qualifications Experience in healthcare, pharma, or life sciences NLP use cases.Background in knowledge graphs, prompt engineering, and multimodal AI.Experience with Reinforcement Learning (RLHF) for improving conversation models.