The AI/ML Developer plays a critical role in the organization's efforts to leverage machine learning and artificial intelligence to develop innovative solutions. They are responsible for designing, implementing, and maintaining machine learning and deep learning algorithms to drive key business initiatives.
Key Responsibilities
Collaborate with cross-functional teams to identify and define machine learning tasks
Develop models and algorithms for natural language processing, computer vision, and predictive analytics
Collect, clean, and preprocess large datasets for model training and evaluation
Build scalable machine learning pipelines and frameworks
Implement and optimize machine learning models in production environments
Conduct performance analysis and fine-tune models to improve accuracy and efficiency
Stay updated with the latest advancements in AI/ML and integrate new technologies into existing systems
Collaborate with the data engineering team to ensure seamless data flow for model training and deployment
Participate in code reviews and contribute to the continuous improvement of the AI/ML development process
Provide technical guidance and mentorship to junior members of the AI/ML team
Required Qualifications
Bachelor's or Master's degree in Computer Science, Data Science, Machine Learning, or a related field
Proven experience in developing and deploying machine learning models using Python and popular libraries (e.g., TensorFlow, PyTorch, scikit-learn)
Solid understanding of machine learning algorithms, neural networks, and deep learning techniques
Proficiency in data analysis, feature engineering, and model evaluation
Experience with cloud platforms for AI/ML deployment (e.g., AWS, Azure, GCP)
Familiarity with version control systems, such as Git
Strong problem-solving skills and the ability to work on complex, large-scale problems
Excellent communication and collaboration abilities to work effectively in a team environment
Experience with deploying machine learning models in production environments
Ability to interpret and present complex technical information to non-technical stakeholders