Sr. Data Scientist (Gen AI) - India (Bangalore, Pune, Hyderabad)

Job Description

💰 Compensation Note: The budget for this role is fixed at INR 50–55 lakhs per annum (non-negotiable). Please ensure this aligns with your expectations before applying.

📍 Work Setup: This is a hybrid role, requiring 3 days per week onsite at the office in Hyderabad, Bangalore or Pune, India.

📝 Interview Process: The process consists of 6 stages, including a technical assessment, code review, code discussion, and panel interviews.

Company Description:

Blend is a premier AI services provider, committed to co-creating meaningful impact for its clients through the power of data science, AI, technology, and people.

With a mission to fuel bold visions, Blend tackles significant challenges by seamlessly aligning human expertise with artificial intelligence. The company is dedicated to unlocking value and fostering innovation for its clients by harnessing world-class people and data-driven strategy.

We believe that the power of people and AI can have a meaningful impact on your world, creating more fulfilling work and projects for our people and clients.

Job Description:

We 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 Responsibilities:

• 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.
• 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.
• 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.
• 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.
  
  

Qualifications:

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.