Staff Data Scientist

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.

Skills
fine-tuning, LLM, Models, Python, "Research Scientist", Finetuning LLM models, Research, transformers

Experience
8-12 yrs