Responsibilities :

- Leading LLMOps and MLOps workstreams across model lifecycle planning, deployment, monitoring, and retraining.

- Managing client-facing data science engagements and aligning delivery plans with project scope, timelines, and budget.

- Guiding teams in building machine learning pipelines, data models, and automated workflows for AI-driven solutions.

- Reviewing technical deliverables for model performance, data quality, and operational readiness across production environments.

- Applying cloud engineering and analytics tools to scale machine learning systems and improve deployment repeatability.

- Coaching team members on AI fluency, data science methods, and troubleshooting approaches for complex implementation issues.

- Translating business needs into practical machine learning solutions that support decision-making and operational improvement.

- Identifying opportunities to refine model monitoring, orchestration, and lifecycle management practices across client projects.

- Developing documentation and technical recommendations that support adoption of machine learning and generative AI capabilities.

- Collaborating with technology and business stakeholders to address risks, dependencies, and delivery challenges.

What Sets You Apart :

Preference for at least one of the following fields of study :

- Artificial Intelligence and Robotics, Business Analytics, Computer and Information Science, Computer Engineering, Computer Programming, Data Processing/Analytics/Science, Engineering, Information Technology, Machine Learning, Management Information Systems, Mathematics, Statistics, Systems Engineering.

- Managing LLMOps and MLOps delivery across client engagements.

- Leading data science teams through planning, execution, and delivery.

- Applying machine learning, deep learning, and NLP techniques.

- Translating complex system interactions into clear delivery plans.

- Mentoring teammates while validating quality, timelines, and standards.