Data Science Role - Generative AI & NLP

Description:

Key Responsibilities:

- Define the AI strategy and roadmap, including tooling selection, experimentation frameworks, and best practices for model development, prompt engineering, and fine-tuning.

- Architect, develop, and deploy large-scale Machine Learning and Generative AI (GenAI) pipelines from ideation through production.

- Lead end-to-end project ownership, from problem identification and data acquisition to model design, evaluation, and deployment.

- Collaborate cross-functionally with engineering, product, and business teams to translate business goals into actionable data science initiatives.

- Mentor and guide junior data scientists, fostering a culture of continuous learning, experimentation, and innovation.

- Evaluate emerging AI technologies and frameworks to enhance organizational capabilities and technical maturity.

Essential Qualifications:

- 3 - 5 years of relevant experience in Data Science, Machine Learning, or Artificial Intelligence.

- Hands-on experience with Large Language Models (LLMs) such as OpenAI GPT, Anthropic Claude, or LLaMA - including prompt engineering, fine-tuning, and embedding-based retrieval systems.

- Expert proficiency in Python and key libraries: NumPy, Pandas, scikit-learn, PyTorch/TensorFlow, and Hugging Face Transformers.

- Proven track record of delivering at least one end-to-end GenAI or advanced NLP project (e.g., custom NER, Q&A systems, text summarization) into production.

- Strong understanding of model deployment and orchestration tools such as Docker, Kubernetes, Airflow, and API frameworks (Flask, FastAPI).

- Solid foundation in statistical modeling, data analysis, and MLOps best practices.

Preferred Qualifications:

- Masters or Ph.D. in Computer Science, Statistics, Data Science, or a related quantitative discipline.

- Experience working in cloud environments (AWS, Azure, or GCP) for scalable model training and deployment.

- Familiarity with vector databases, retrieval-augmented generation (RAG), or multi-modal AI systems.

- Strong communication skills with the ability to translate complex technical findings into actionable business insights