Assistant Vice President - Data Science & Artificial Intelligence - Large Language Models & Cloud Platform

Key Responsibilities:

- Lead and manage a high-performing AI/ML team, providing technical guidance and mentorship.

- Design, develop, and deploy Large Language Models (LLMs) and RAG systems for business applications.

- Oversee MLOps processes, ensuring robust model deployment, monitoring, version control, and scalability.

- Integrate AI solutions into enterprise workflows and business processes for maximum impact.

- Collaborate with product, engineering, and business teams to align AI initiatives with organizational goals.

- Evaluate and implement cutting-edge AI/ML technologies, including TensorFlow, PyTorch, Hugging Face, and Scikit-learn.

- Ensure compliance with data governance, ethical AI principles, and industry best practices.

- Drive continuous improvement and innovation in AI solutions, workflows, and methodologies.

Core Skills:

- Artificial Intelligence & Machine Learning (AI/ML)

- Large Language Models (LLMs) & RAG Systems

- MLOps & Model Lifecycle Management

- NLP & Predictive Modeling

- Team Leadership & Strategic Planning

Technical Skills:

- Programming & Frameworks: Python, TensorFlow, PyTorch, Scikit-learn

- NLP & LLM Tools: Hugging Face Transformers, RAG frameworks

- Cloud Platforms: GCP, AWS, Azure

- MLOps & Workflow Tools: MLflow, Airflow, Docker, Kubernetes

- Data Handling & Analysis: Pandas, NumPy, SQL, BigQuery

Education & Experience:

- Bachelor's or Master's degree in Computer Science, Data Science, AI, or related field.

- PhD preferred for research-focused initiatives.

- 5+ years of experience in AI/ML, with hands-on experience in LLMs, RAG systems, MLOps, and cloud deployments.

- Proven experience in leading technical teams and delivering enterprise-scale AI projects.

KPIs / Success Metrics:

- Performance metrics of deployed AI/ML models (accuracy, F1 score, recall, etc.)

- Efficiency and reliability of MLOps pipelines and model deployment

- Business impact from AI solutions (e.g , automation, cost reduction, revenue growth)

- Number of AI/ML initiatives successfully delivered on time and within scope

- Adoption rate and integration of AI solutions across business units