Lead Data Scientist - Generative AI

Description :

Key Responsibilities :

- Define and execute the AI roadmap, selecting the right tooling, frameworks, and methodologies for model building, prompt engineering, and fine-tuning.

- Architect, develop, and deploy large-scale ML and Generative AI pipelines, transforming ideas into production-ready systems.

- Lead end-to-end project delivery, from opportunity identification and stakeholder collaboration to model evaluation, deployment, and results communication.

- Mentor and manage junior and mid-level data scientists, fostering a strong culture of innovation, collaboration, and continuous learning.

- Partner with engineering, product, and business teams to translate complex business problems into scalable AI/ML solutions.

- Stay current with emerging AI trends and technologies, incorporating cutting-edge methods into practical applications.

Essential Qualifications :

- 58 years of experience in Data Science, Machine Learning, or AI, including at least 2 years in a technical leadership or team lead capacity.

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

- Proven experience delivering end-to-end Generative AI or advanced NLP projects (e., custom NER, Q&A systems, document summarization, conversational AI) into production.

- Familiarity with deployment, orchestration, and workflow management tools such as Docker, Kubernetes, Airflow, and API frameworks (Flask, FastAPI).

- Strong understanding of data pipelines, MLOps practices, and model monitoring in production environments.

Preferred Qualifications :

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

- Experience with cloud platforms (AWS, GCP, or Azure) and vector databases (e., FAISS, Pinecone, Weaviate).

- Knowledge of Retrieval-Augmented Generation (RAG), multi-modal models, or LLM evaluation frameworks.

- Excellent communication and stakeholder management skills with the ability to translate technical insights into business value