Responsibilities:

• You will assist in analyzing structured and unstructured data to extract valuable insights using statistical techniques

• You will contribute to the development and evaluation of predictive analytics models to enhance business outcomes.

• You will work with product teams to gather and understand data/feature store requirements for model development.

• You will apply statistical methods, ML, and NLP techniques to address complex yet custom data problems.

• You will support the implementation and management of LLMOps (Large Language Model Operations) and MLOps (Machine Learning Operations), including creating and maintaining ML CI/CD pipelines.

• You will monitor model performance and data accuracy, and provide recommendations for continuous improvement.

• You will collaborate with cross-functional teams to identify, troubleshoot, and resolve customer issues through clear and concise stakeholder communication.

• You adhere to zeotap’s company, privacy and information security policies and procedures

• You complete all the awareness training assigned on time.

• Bachelor's/Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.

• Experience in Machine Learning Operations (MLOps), particularly in building and managing ML pipelines.

• Familiarity with any cloud platforms, especially experience with GCP is a plus.

• Knowledge in Natural Language Processing (NLP) with experience using libraries such as langchain, Hugging Face, LLM evals, vector databases, and FAISS.

• Working knowledge of SQL is a plus.

• Strong problem-solving skills and advanced programming skills in Python.

• Proficiency in at least one of the following frameworks: TensorFlow, PyTorch, SKLearn.

• Strong mathematical foundation in Linear Algebra, Probability and Statistics, Calculus, and Optimization.

• Strong foundation in Machine Learning and Deep Learning.

• Familiarity with NLP Concepts like Named Entity Recognition, Text Embedding Creation, Prompt Engineering, Similarity Search, Transformer Architecture.