Staff Data Scientist

OneShot AI; Bengaluru; On-site; INR 3,500,000 - 6,500,000; Full time

Job summary

OneShot AI in Bengaluru seeks a senior AI engineer to design and deploy NLP and GenAI solutions across real-world applications. You will develop transformer-based models, work with LLMs, and build scalable pipelines from experimentation to production.

You will contribute to production-grade Python code, collaborate with product teams, and stay ahead of GenAI frameworks and ecosystems while delivering robust AI platforms.

Qualifications

• 6+ years of experience in Data Science, Machine Learning, or AI Engineering.
• Strong background in NLP and deep learning.
• Hands-on experience building and deploying ML/AI models in production.
• Strong experience with transformer architectures and modern NLP frameworks.
• Proven experience working with LLMs and Generative AI systems.
• Strong coding skills in Python and experience with ML/AI libraries such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, or similar frameworks.
• Experience with fine-tuning, embeddings, vector databases, RAG pipelines, and prompt engineering.
• Solid understanding of data structures, algorithms, experimentation, and model evaluation techniques.
• Ability to independently drive projects in ambiguous and fast-paced environments.

Responsibilities

• Design, build, and deploy NLP and Generative AI solutions for real-world applications.
• Develop and optimize machine learning and deep learning models, including transformer-based architectures.
• Work with LLMs, prompt engineering, fine-tuning, RAG pipelines, and agentic workflows.
• Build scalable model pipelines from experimentation to production deployment.
• Collaborate with engineering and product teams to translate business problems into AI solutions.
• Conduct model evaluation, experimentation, benchmarking, and performance optimization.
• Write production-quality Python code and contribute to AI platform architecture.
• Stay up to date with advancements in LLMs, GenAI frameworks, and open-source AI ecosystems.

Skills

Python

NLP

Deep learning

Data science fundamentals

Ambiguity navigation

Tools

PyTorch

TensorFlow

Hugging Face Transformers

LangChain

Job description

Responsibilities:

• Design, build, and deploy NLP and Generative AI solutions for real-world applications.
• Develop and optimize machine learning and deep learning models, including transformer-based architectures.
• Work extensively with Large Language Models (LLMs), prompt engineering, fine‑tuning, RAG pipelines, and agentic workflows.
• Build scalable model pipelines from experimentation to production deployment.
• Collaborate with engineering and product teams to translate business problems into AI solutions.
• Conduct model evaluation, experimentation, benchmarking, and performance optimization.
• Write production-quality Python code and contribute to AI platform architecture.
• Stay up to date with the latest advancements in LLMs, GenAI frameworks, and open-source AI ecosystems.

Requirements:

• 6+ years of experience in Data Science, Machine Learning, or AI Engineering.
• Strong background in Natural Language Processing (NLP) and deep learning.
• Hands‑on experience building and deploying ML/AI models in production.
• Strong experience with transformer architectures and modern NLP frameworks.
• Proven experience working with Large Language Models (LLMs) and Generative AI systems.
• Strong coding skills in Python and experience with ML/AI libraries such as PyTorch, TensorFlow, Hugging Face Transformers, LangChain, or similar frameworks.
• Experience with model fine‑tuning, embeddings, vector databases, RAG pipelines, and prompt engineering.
• Solid understanding of data structures, algorithms, experimentation, and model evaluation techniques.
• Ability to independently drive projects in ambiguous and fast‑paced environments.

Preferred Qualifications:

• Experience with multi‑agent systems, AI orchestration, or autonomous workflows.
• Exposure to distributed training, inference optimization, or scalable AI infrastructure.
• Experience deploying AI systems on cloud platforms such as AWS, GCP, or Azure.
• Familiarity with MLOps, CI/CD pipelines, and monitoring production AI systems.
• Research or applied experience in conversational AI, semantic search, summarization, or recommendation systems.

Ideal Candidate:

• The ideal candidate combines strong data science fundamentals with hands‑on GenAI engineering expertise.
• You should be comfortable moving from research and experimentation to scalable production systems while maintaining high ownership, speed, and technical rigor.