Senior Data Scientist
Uber · Bengaluru
- Experience7–11 yrs
- SalaryNot disclosed
- Work modeonsite
- Levelexecutive
- Posted3 Sept 2026
About Uber
Uber is hiring in Bengaluru in automotive mobility. This role looks for around 7+ years of experience.
Skills
- Python
- machine learning
- large language models
- agentic workflows
- deep learning
- PyTorch
- TensorFlow
- Keras
- Natural Language Processing
- NumPy
- SciPy
- Pandas
- scikit-learn
- AWS
- Google Cloud Platform
- Microsoft Azure
- microservices
- data engineering
- data analysis
- hyperparameter tuning
- multimodal data
- unstructured data
- statistical regression
- neural networks
- decision trees
- support vector machines
- ensemble methods
The role
A generative AI engineer at a mobility marketplace builds machine learning solutions with Python and develops agentic workflows using large language models for unstructured and multimodal data. The role also applies PyTorch and TensorFlow to create, deploy, and monitor production AI systems.
Full job description
Responsibilities:
Analyze product requirements and come up with data science solutions.
Identify and develop data engineering scripts (example: parsers) necessary to build training datasets.
Build deep learning NLP model(s), customize as needed to meet the requirements.
Write production-quality Python code for model development as well as for inference.
Ability to think out-of-the-box and implement custom loss functions and quantitative methods to increase the accuracy of AI solutions.
Build and ship AI agent-driven systems that reason, plan, and act across real-world, messy data.
Design agentic workflows using LLMs, tools, retrieval, and memory to solve high-impact product problems.
Work hands-on with unstructured and multimodal data (text, PDFs, drawings, images, logs).
Develop multimodal models (text + vision) for document understanding and contextual reasoning.
Own AI solutions end-to-end: data pipelines, modeling, deployment, monitoring, and iteration.
Write production-grade Python code and rapidly prototype, test, and ship in a cloud-native environment.
Take complete ownership of the solution in all phases: analysis, proof of concept, data engineering, model development, model tuning, and model implementation.
Requirements:
7+ years of experience building production ML / AI systems, with recent experience in LLMs or AI agents in data science.
Strong Python engineer with a bias toward clean, scalable, and maintainable code.
Proven experience working with unstructured data and images at scale.
Hands-on experience with agent patterns (tool use, function calling, planning, memory).
Good proficiency and hands-on experience with LLMs and advanced AI models
Experience deploying AI systems on AWS, GCP, or Azure in microservices architectures.
Thrives in fast-moving startup environments with high ownership and ambiguity.
Strong Python skills with a focus on data engineering and data analysis.
Proficiency with data mining algorithms such as Scikit-Learn, NumPy, SciPy, and Pandas.
Strong understanding of machine learning models, model training, and hyperparameter tuning.
Working knowledge of deep learning models, loss functions, and accuracy measures.
Hands-on proficiency with PyTorch / TensorFlow / Keras.
Experience in NLP solutions preferred.
Experience in parsing PDF and text documents preferred.
Experience with statistical regression, neural nets, deep learning, decision trees, SVM, and ensembles is expected.
Multi-cloud experience and proficiency with providers AWS, GCP, or Azure
Comfortable working in a microservices environment
Self-motivated, enthusiastic to build next-generation AI systems.