| work to be accomplished | Data Management & Preparation
Gather, clean, and preprocess both structured and unstructured data for use in AI/ML workflows.
Apply data governance standards, conduct quality checks, and handle metadata management tasks.
Manage large datasets utilizing SQL, NoSQL, and distributed data technologies.
AI/ML Development
Build, train, and validate machine learning models using tools such as AutoML, deep learning, and reinforcement learning frameworks.
Enhance models for improved performance, scalability, and accuracy.
Employ feature engineering and model evaluation strategies.
Architecture & Integration
Support the design of AI/ML architectural solutions that comply with enterprise guidelines.
Deploy models into production settings by leveraging APIs, microservices, or containerized approaches.
Aid deployments on cloud services (AWS, Azure) and make use of MLOps methodologies.
Collaboration & Documentation
Collaborate with multidisciplinary teams, including data engineers, analysts, and business partners.
Maintain records of workflows, models, and architecture choices to facilitate knowledge sharing. |
| Minimum Yrs of Experience, Skills, and Qualifications | 2 years of AI/ML development 2 years of data management experience 2 years experience using Python and associated libraries (Pandas, NumPy, TensorFlow, PyTorch) Solid grasp of data structures, algorithms, and relational database concepts. 1 year experience with cloud platforms (Azure/AWS/Google Cloud Platform) and container technologies (Docker/Kubernetes) Excellent problem-solving abilities and communication skills |
