Data Science Lead

Title: Data Science Lead

Location : Remote

Job Description:

We are seeking an experienced Data Science Lead to spearhead the development, deployment, and scaling of Large Language Models (LLMs) within our AI ecosystem. This role requires a hands-on leader with deep expertise in LLM operationalization, prompt engineering, Databricks, and Azure AI services. You will lead initiatives in fine-tuning models, Retrieval-Augmented Generation (RAG), and transforming LLM prototypes into production-grade solutions that drive business impact.

Key Responsibilities:

Lead and scale LLM-based projects from proof-of-concept (PoC) to full-scale production across the organization, focusing on delivering measurable business value.

Productionalize LLMs on Azure, ensuring that models are robust, efficient, and scalable in live environments.

Develop and optimize prompt engineering techniques to enhance model performance, reduce costs, and improve the accuracy of outputs.

Collaborate with data engineers and architects to integrate LLMs with data pipelines on platforms such as Databricks for scalable processing.

Implement Retrieval-Augmented Generation (RAG) methods to improve the relevance and accuracy of LLM outputs, leveraging both structured and unstructured data sources.

Oversee fine-tuning of pre-trained LLMs to create domain-specific models that address business challenges in various verticals.

Drive continuous improvement and experimentation with LLM models, staying at the forefront of the latest advancements in Generative AI.

Ensure model governance, monitoring, and AI/ML Ops practices for safe and reliable deployment in production environments.

Collaborate with cross-functional teams, including product managers, engineers, and other stakeholders, to define project requirements, timelines, and deliverables.

Provide mentorship and guidance to junior data scientists, fostering a culture of innovation and knowledge sharing.

Required Skills and Qualifications:

Experience with LLMs and Generative AI: Deep knowledge of Large Language Models (GPT, BERT, etc.) and their deployment at scale.

Azure AI Expertise: Proven experience with Azure Machine Learning, Azure Cognitive Services, and other cloud-native AI solutions for deploying models.

Prompt Engineering: Strong experience in prompt tuning and engineering techniques to refine outputs of LLMs.

Databricks: Hands-on experience with Databricks for big data processing, model training, and inference at scale.

Retrieval-Augmented Generation (RAG): Proficiency in implementing RAG techniques to improve the quality and relevance of model outputs.

Fine-Tuning: Experience in fine-tuning pre-trained models to meet specific business or domain needs.

Strong understanding of AI/ML Ops for continuous integration, deployment, and monitoring of models in production.

Ability to translate complex technical concepts into practical business solutions.

Leadership Experience: Proven ability to lead data science teams and collaborate across functions to deliver AI-driven solutions.

Excellent communication and stakeholder management skill