Looking for a Machine Learning (ML) Engineer to help create and launch machine learning models and other smart solutions. In this role, you'll work with an AI Architect and different teams to develop systems that solve tough business problems and make customers happy.

Key Responsibilities:

Build ML Models: Create, train, and test machine learning models using popular tools like TensorFlow, PyTorch, and scikit-learn.
Prepare Data: Collect, clean, and organize large datasets for training and testing ML models.
Feature Engineering: Find and create the most useful data features to improve model performance.
Improve Algorithms: Test and apply advanced methods for tasks like classification, regression, clustering, and detecting unusual patterns.
Integrate Models: Work with software developers to make sure ML models work well in real-world systems.
Evaluate Performance: Check how well models perform, improve them, and ensure they work quickly and accurately.
Deploy Models: Help set up systems for deploying and monitoring ML models in production environments.
Stay Updated: Keep up with the latest AI/ML developments and suggest new ideas.
Collaborate: Work with data engineers, product teams, and others to understand their needs and create tailored solutions.

Qualifications:

Education:

Bachelor's degree in Computer Science, Data Science, Artificial Intelligence, or a related field.

Experience:

3 to 6 years of experience in building and deploying machine learning models.

Technical Skills:

Proficient in Python and ML tools like scikit-learn, TensorFlow, and PyTorch.
Experience with data tools like Pandas and NumPy, and visualization libraries like Matplotlib or Seaborn.
Familiarity with big data tools like Hadoop and Spark (a plus).
Knowledge of SQL/NoSQL databases and tools like Apache Airflow for data pipelines.
Experience with cloud platforms such as AWS, Azure, or Google Cloud and their AI/ML services.
Understanding of different ML approaches, including supervised and unsupervised learning, deep learning, and reinforcement learning.
Experience with MLOps and setting up deployment pipelines.

Soft Skills:

Strong problem-solving and analytical abilities.
Good communication and teamwork skills.
Ability to work in a fast-moving and collaborative environment.