Role & responsibilities

Responsibilities

- Developing and deploying LLMOps and MLOps workflows that support model training, testing, deployment, and monitoring across client solutions

- Translating advanced data science and machine learning requirements into scalable automation, orchestration, and lifecycle management approaches

- Applying statistical analysis, predictive modeling, and data engineering techniques to improve model performance and operational reliability

- Supporting client engagements by gathering technical requirements, interpreting feedback, and shaping practical implementation plans

- Building data pipelines and data quality controls that support machine learning use cases and AI-enabled solutions

- Evaluating model behavior, retraining needs, and drift signals by using monitoring tools and analytical methods

- Documenting technical decisions, solution design, and deployment steps to support transparency and reuse across project workstreams





- Mentoring junior team members on machine learning workflows, AI tooling, and delivery methods while contributing to team development

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

- Demonstrating LLMOps and MLOps delivery across client engagements

- Utilizing AI tooling, data pipelines, and model monitoring

- Interpreting data to guide recommendations and delivery priorities

- Building client connections while navigating ambiguity and change

- Mentoring junior team members through complex technical work

Preferred candidate profile

📌 LLMOps + MLOps - Senior Associate-Healthcare/Lifescience/Pharma (Bengaluru)
🏢 PwC
📍 Bengaluru