Lead Data Scientist (Gen AI)

IDFC FIRST Bank · Bengaluru

  • Experience7–11 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Levelexecutive
  • 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 7+ years of experience.

Skills

  • Python
  • R
  • pandas
  • NumPy
  • scikit-learn
  • TensorFlow
  • PyTorch
  • Keras
  • NLP
  • NLTK
  • spaCy
  • Gensim
  • BERT
  • GPT
  • LLMs
  • MLOps
  • SQL
  • NoSQL
  • Apache Airflow
  • Control-M
  • AWS
  • Azure
  • Google Cloud Platform

The role

A generative AI engineer at a banking and financial services company leads Gen AI projects and builds NLP solutions using Python, transformer models, and MLOps. The role also guides text analytics systems and mentors data science teams.

Full job description

The core responsibilities for the job include the following:

Key / Primary Responsibilities:

Leading the end-to-end lifecycle of advanced Gen AI projects, including scoping, solution design, delivery, and deployment within cross-functional teams.

Defining data science best practices, code quality standards, and technical governance across the team.

Expert-level proficiency in Python or R, with deep experience in data science libraries such as pandas, NumPy, Scikit-Learn, TensorFlow, PyTorch, and Keras.

Overseeing the adoption and optimization of Natural Language Processing (NLP) solutions, including frameworks like NLTK, SpaCy, Gensim, as well as advanced transformer models (BERT, GPT, LLMs).

Architecting and guiding the implementation of large-scale text analytics systems (topic modeling, sentiment analysis, text classification, and semantic search).

Ensuring robust model management, reproducibility, and effective MLOps in cloud (AWS, Azure, GCP) and on-premise infrastructures.

Advanced SQL and NoSQL expertise, and hands-on use of orchestration and workflow automation tools (Airflow, Control-M).

Secondary Responsibilities:

Mentoring and upskilling data scientists, reviewing code and solutions, and providing technical leadership on multiple concurrent projects.

Shaping data flow designs for large-scale, unstructured data; championing data quality and integrity standards.

Driving model validation, monitoring, and continuous improvement for production workloads.