Fundamental knowledge of supervise and unsupervised learning and their
applications inreal world business scenarios.
Familiarity with End-2-End Machine Learning model development life cycle.
Experience in creating data processing pipelines and API development (Fast API).
Experience with deploying and maintaining ML/DL models in production,
model monitoring, and knowledge of concept/data drift, model
reproducibility, code, model, and data versioning.
Experience with SQL and NoSQL, MLflow, GitHub, docker, Kubernetes, ELK, or similar stack.
Hands on with text and image processing: cleaning, transformations, and data preparationfor modelling. And should be comfortable with libraries like Pandas, NumPy, OpenCV, PIL, Spacy, transformers etc.
Should have working knowledge of machine learning frameworks like Scikitlearn, PyTorch/ Tensorflow/Keras.
Experience with cloud computing platforms like GCP and AWS.
Should understand LLMs, Open AI API, model fine-tuning and prompt engineering.
Exposure to CI/CD principles and associated tools.
Good to have:
Experience with deep learning frameworks
Strong experience in programming and statistics
Excellent verbal and written communication skills
Highly developed attention to detail
Strong presentation skills
Ability to work well in a team environment
Excellent problem-solving skill