>>> Bachelor’s or Master’s degree in Data Science, Computer Science, Mathematics, Statistics, or a related field.
>>> Advanced proficiency in Python programming with a focus on writing clean, testable and efficient code.
>>> DevOps & Containers: Proficient with Docker for containerization and working knowledge of Kubernetes (k8s) for orchestration.
>>> Practical understanding of GPU architecture and cloud compute instances to optimize resource allocation for training and inference workloads.
>>> MLOPS tools: hands on experience with MLflow (or similar tools like weights & biases) for experiment tracking and model registry.
>>> Proven experience working with Large Language Models (LLMs).
>>> Good understanding of AI agents & agentic workflows, LLM orchestration frameworks and reasoning patterns.
>>> Experience with data preprocessing, feature engineering, and model selection and evaluation techniques.
>>> Hands-on experience with CI/CD pipelines (GitLab, Jenkins).
>>> Knowledge of statistical and mathematical concepts relevant to machine learning, such as probability, linear algebra, and optimization.
>>> Excellent problem-solving and debugging skills, with the ability to identify and resolve issues quickly and effectively.
>>> Relevant work experience in machine learning, data science or a related field.