Conversational AI & Call Transcription Development
- Develop and fine-tune automatic speech recognition (ASR) models
- Implement language model fine-tuning for industry-specific language.
- Develop speaker diarization techniques to distinguish speakers in multi-speaker conversations.
2.NLP & Generative AI Applications
- Build 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 Support
- Develop 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 & Scalability
- Deploy 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.
Technical 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.
