Data Scientist - Python/NLP

Job Title : Data Scientist

Experience : 6 to 10 Years

Location : Noida, Bangalore, Pune

Employment Type : Full-time

Job Summary :

We are seeking a highly skilled and experienced Data Scientist with a strong background in Natural Language Processing (NLP), Generative AI, and Large Language Models (LLMs). The ideal candidate will be proficient in Python and have hands-on experience working with both Google Cloud Platform (GCP) and Amazon Web Services (AWS). You will play a key role in designing, developing, and deploying AI-driven solutions to solve complex business problems.

Key Responsibilities :

- Design and implement NLP and Generative AI models for use cases such as chatbots, text summarization, question answering, and information extraction.

- Fine-tune and deploy Large Language Models (LLMs) using frameworks such as Hugging Face Transformers or LangChain.

- Conduct experiments, evaluate model performance, and implement improvements for production-scale

solutions.

- Collaborate with cross-functional teams including product managers, data engineers, and ML engineers.

- Deploy and manage ML models on cloud platforms (GCP and AWS), using services such as Vertex AI,

SageMaker, Lambda, Cloud Functions, etc.

- Build and maintain ML pipelines for training, validation, and deployment using CI/CD practices.

- Communicate complex technical findings in a clear and concise manner to both technical and non-technical stakeholders.

Required Skills :

- Strong proficiency in Python and common data science/ML libraries (NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch).

- Proven experience in Natural Language Processing (NLP) techniques (NER, sentiment analysis, embeddings,

topic modeling, etc.).

- Hands-on experience with Generative AI and LLMs (e.g., GPT, BERT, T5, LLaMA, Claude, Gemini).

- Experience with LLMOps, prompt engineering, and fine-tuning pre-trained language models.

- Experience with GCP (BigQuery, Vertex AI, Cloud Functions, etc.) and/or AWS (SageMaker, S3, Lambda, etc.).

- Familiarity with containerization (Docker), orchestration (Kubernetes), and model deployment best practices