Collaborate with data science and engineering teams to develop, optimize, and deploy machine learning models in on premises and cloud environments.
Oversee the ML model lifecycle from data preparation and feature engineering to deployment and monitoring.
Prepare, clean, and transform data to be used for training and inference.
Ensure the scalability, reliability, and performance of machine learning models.
Monitor and maintain deployed models, implement updates, and troubleshoot any issues that may arise.
Assist in designing and maintaining machine learning pipelines, ensuring efficient data flow and model deployment.
Translate analytical findings into clear recommendations for stakeholders.
Ensure responsible AI practices are followed, addressing bias, fairness, and ethical considerations in line with Data Privacy, Cybersecurity, and AI policies.
Minimum Requirements
Bachelor’s degree holder of any quantitative discipline such as Data Science, Statistics, Computer Science, or related quantitative field
2–4+ years’ experience with the full ML/AI lifecycle from data preparation to deployment and monitoring
Strong proficiency in Python, SQL, and experience with AWS/Databricks environments
Good communication skills to explain complex ML/AI concepts to diverse stakeholders
Exposure to Generative AI or LLMs, with understanding of responsible AI practices (bias, fairness, data privacy)
Nice to Have:
Hands-on experience with LLMOps, RAG, or Agentic AI
Version Control (git) and experience with CI/CD pipelines for ML model deployment