Senior Data Scientist

IDFC FIRST Bank · Bengaluru

  • Experience3–7 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Levelmid
  • Posted2 Sept 2026
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About IDFC FIRST Bank

IDFC FIRST Bank is hiring in Bengaluru in financial services. This role looks for around 3+ years of experience.

Skills

  • NLP
  • Conversational AI
  • Generative AI
  • large language models
  • speech-to-text
  • text-to-speech
  • retrieval-augmented generation
  • prompt engineering
  • fine-tuning
  • PyTorch
  • TensorFlow
  • Hugging Face
  • Python
  • SQL
  • NoSQL

The role

A generative AI engineer at a banking and financial services company builds conversational AI systems for voice-based customer experiences, applying NLP and large language models to speech pipelines, retrieval-augmented generation, and production deployment. The role also develops prompt engineering and multimodal AI solutions for sales, collections, and customer experience.

Full job description

We are looking for a high-impact Data Scientist (3-10 years) to build and scale GenAI-powered Voice and Conversational AI systems for real-world enterprise use cases across BFSI. You will work on cutting-edge LLMs, speech (STT/TTS), and multimodal AI, owning the lifecycle from model design experimentation to production deployment. This is a hands-on IC role with strong ownership and direct business impact (sales, collections, customer experience). If you've built production-grade conversational AI systems and enjoy solving latency, accuracy, and real-world deployment challenges, this role is for you.

Responsibilities:

Build and deploy GenAI-powered voice bots and conversational AI systems.

Designed end-to-end voice pipelines (STT LLM TTS)with focus on latency and quality.

Evaluate and optimize LLMs for voice-first use cases (sales, collections, CX).

Develop RAG pipelines, prompt strategies, and fine-tuned models.

Improve conversation quality, tool calling accuracy, and response reliability.

Design conversation workflows, intents, and dialogue systems.

Work with large-scale conversational datasets (text, transcripts, and audio).

Mitigate risks: hallucination, bias, safety, and privacy.

Collaborate with Product, ML Engineering, and Platform teams for production deployment.

Experiment with multimodal models, agentic workflows, and tool-augmented LLMs.

Key Success Metrics:

Production deployment of high-quality voice AI systems.

Improvements in conversation success rate and user experience.

Low latency, high accuracy, and system reliability.

Measurable impact on business metrics (sales, collections, CX).

Requirements:

3-10 years of experience in NLP/Conversational AI / GenAI.

Hands-on experience building voice bots or conversational AI systems in production.

Strong understanding of LLMs (GPT, LLaMA, etc. ), RAG, prompt engineering, fine-tuning, Speech systems (STT/TTS pipelines).

Experience with deep learning frameworks (PyTorch / TensorFlow / Hugging Face).

Strong coding in Python + data handling (SQL/NoSQL).

Experience working with large-scale datasets (text/audio).

Understanding of latency, scalability, and production constraints.

Good to Have:

Experience with multimodal AI / audio token-based models.

Exposure to agentic AI frameworks (LangChain, LlamaIndex, etc. ).

Cloud ML platforms (AWS SageMaker / Azure ML / GCP).

Prior experience in BFSI / customer-facing AI systems.