Senior Data Scientist

This role is for one of Weekday’s clients
Salary range: Rs 1800000 - Rs 5000000 (ie INR 18 - 50 LPA)

Min Experience: 3+ years
Location: MUMBAI, Bengaluru, Karnataka, India
JobType: full-time

We are looking for an experienced Senior Data Scientist to design, develop, and deploy Generative AI solutions, with a strong focus on voice bots and conversational AI systems. The ideal candidate will have hands-on experience building production-ready AI applications using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), speech technologies, and advanced machine learning techniques.

In this role, you will build intelligent voice-first systems for use cases such as sales, collections, and customer experience (CX). You will be responsible for improving conversational quality, reducing response latency, enhancing model reliability, and developing scalable AI pipelines. You will collaborate with product, machine learning engineering, and platform teams to transform research and experimentation into reliable, production-grade solutions.

Requirements

Key Responsibilities

1. Generative AI and Conversational AI Development

• Design, build, and deploy Generative AI-powered voice bots and conversational AI applications for enterprise use cases.
• Develop intelligent conversational systems that support natural, context-aware, and reliable interactions.
• Evaluate and integrate LLMs such as GPT, LLaMA, and other suitable foundation models.
• Develop prompt engineering strategies, fine-tuning approaches, and context-management techniques to improve model performance.

2. Voice Pipeline Engineering

• Design and optimise end-to-end voice processing pipelines involving Speech-to-Text (STT), Large Language Models, and Text-to-Speech (TTS).
• Improve response latency, speech quality, conversational flow, and overall user experience.
• Integrate speech recognition and speech synthesis technologies with LLM-powered applications.
• Identify and resolve performance bottlenecks across voice processing and inference workflows.

3. RAG and LLM Optimisation

• Build and maintain RAG pipelines to enable accurate, context-aware responses using relevant knowledge sources.
• Develop retrieval strategies, prompt templates, and contextual grounding mechanisms to improve response quality.
• Evaluate and optimise LLM performance for voice-first applications and domain-specific business requirements.
• Improve tool-calling accuracy, response consistency, and the reliability of multi-step conversational workflows.
• Experiment with model fine-tuning and other optimisation techniques to enhance performance.

4. Conversation Design and Workflow Development

• Design conversational workflows, dialogue systems, intents, and interaction patterns for voice-based applications.
• Improve conversation handling, context retention, error recovery, and response relevance.
• Develop tool-augmented LLM applications and agentic workflows that can execute tasks and interact with external systems.
• Analyse conversation outcomes and user interactions to identify opportunities for improvement.

5. Data Processing and Model Evaluation

• Work with large-scale conversational datasets, including text, call transcripts, and audio data.
• Develop data processing, cleaning, preparation, and evaluation pipelines for AI model development.
• Establish evaluation frameworks to measure response quality, relevance, latency, accuracy, and task completion.
• Conduct experiments and analyse model outputs to identify performance gaps and validate improvements.

6. AI Safety, Reliability, and Production Deployment

• Implement strategies to mitigate hallucinations, bias, unsafe responses, and privacy risks.
• Improve the reliability, robustness, and consistency of AI-powered conversational systems.
• Collaborate with Product, ML Engineering, and Platform teams to deploy and maintain AI solutions in production.
• Monitor system performance and continuously improve models, pipelines, and conversational experiences.
• Explore emerging technologies, including multimodal AI models and advanced agentic systems.

Must-Have Skills

• Generative AI: Strong practical understanding of LLMs and experience developing production-oriented Generative AI applications.
• Retrieval-Augmented Generation (RAG): Experience building retrieval pipelines and grounding LLM responses in relevant information.
• Conversational AI: Hands-on experience developing and deploying voice bots or conversational AI systems in production.
• LLM Optimisation: Knowledge of prompt engineering, model evaluation, fine-tuning, and improving response quality.
• Speech Technologies: Understanding of STT and TTS systems and their integration into end-to-end voice pipelines.
• Python Programming: Strong coding skills for AI development, experimentation, data processing, and deployment.
• Machine Learning: Solid understanding of machine learning and deep learning concepts.

Good-to-Have Skills

• Experience with PyTorch, TensorFlow, and Hugging Face.
• Familiarity with GPT, LLaMA, and other open-source or commercial LLMs.
• Experience with SQL and NoSQL databases and large-scale data handling.
• Knowledge of multimodal AI, AI agents, tool calling, and workflow orchestration.
• Experience optimising inference latency, model performance, and production AI systems.
• Familiarity with responsible AI practices, privacy controls, and conversational safety mechanisms.
• Experience building AI solutions for sales automation, collections, customer support, or customer experience.

Required Qualifications and Experience

• 3–10 years of experience in Data Science, Natural Language Processing (NLP), Conversational AI, Generative AI, or a related field.
• Demonstrated hands-on experience building and deploying voice bots or conversational AI systems in production.
• Strong understanding of LLM architectures, RAG pipelines, prompt engineering, and fine-tuning techniques.
• Practical experience designing speech-enabled AI applications using STT and TTS components.
• Proficiency in Python and experience working with structured and unstructured data.
• Familiarity with deep learning frameworks and modern AI development tools.
• Strong analytical, problem-solving, and experimentation skills.
• Ability to collaborate with cross-functional teams and translate business requirements into effective AI solutions.