•    Minimum 6+ years of relevant experience in building software applications in data and analytics field, leveraging big data and cloud platforms etc.
•    Extensive hands-on experience with Python, R, or Julia, focusing on data science and machine learning frameworks.
•    Expertise in working with NLP libraries such as Hugging Face Transformers, SpaCy, NLTK, and Stanford NLP.
•    Experience with Reinforcement Learning with Human Feedback (RLHF) and prompt engineering for task optimization.
•    Strong expertise in integrating LLMs into CI/CD pipelines and establishing robust MLOps practices for versioning, monitoring, and retraining.
•    Familiarity with tools like MLflow, Kubeflow, or TFX for managing ML lifecycle and performance.
•    Experience in building and maintaining efficient data pipelines for pretraining and fine-tuning LLMs, leveraging Spark, Airflow, or Snowflake.
•    Proficiency in leveraging cloud platforms (AWS, Azure, GCP) for hosting LLMs and utilizing tools like SageMaker, Vertex AI, or Dataproc.
•    Experience in distributed training and inference of LLMs using techniques like model parallelism and sharding.