Responsibilities

- Supporting LLMOps and MLOps activities across data science and data engineering initiatives

- Building and maintaining machine learning pipelines for model development, testing, deployment, and monitoring

- Automating workflow steps using cloud-based tools and digital platforms to streamline model operations

- Collaborating across technical teams to gather requirements and translate them into operational model support tasks

- Documenting model inputs, outputs, dependencies, and run procedures to support repeatable execution

- Analyzing model performance data and identifying issues that affect reliability, scalability, or data quality

- Applying statistical analysis, machine learning concepts, and programming skills to troubleshoot model behavior

- Contributing to data preparation, feature creation, and data pipeline activities for analytics solutions

- Supporting natural language processing, deep learning, and predictive modeling work across client projects





- Learning recent tools and methods quickly while taking ownership of assigned deliverables and timelines

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 coursework or projects in machine learning and data science

- Applying foundational knowledge of LLMOps or MLOps concepts

- Using Python, SQL, or cloud tools in academic or project settings

- Gathering and interpreting data from varied sources

- Communicating ideas clearly and adapting across team settings

📌 LLMOps + MLOps - Associate (Bengaluru)
🏢 PwC
📍 Bengaluru