Job role:
We are looking a talented Machine learning engineer with a focus on Artificial Intelligence, to join Promptly. The ideal candidate will have a strong background in data science, machine learning, and AI technologies. This role requires a deep understanding of AI algorithms, natural language processing, computer vision, and the ability to apply AI techniques to solve complex business problems.
Responsibilities:
- Ensure that data flows smoothly from source to destination so that it can be processed
- Develop and implement AI models to solve business challenges.
- Gather, preprocess, and curate data for AI model development.
- Filter and cleanse unstructured (or ambiguous) data into usable data sets that can be analyzed to extract insights and improve business processes.
- Evaluate AI model performance using relevant metrics and optimize models for improved accuracy and efficiency.
- Continuously explore and implement new techniques to enhance AI capabilities.
- Identify new internal and external data sources to support analytics initiatives and work with appropriate partners to absorb the data into new or existing data infrastructure
- Build tools for automating repetitive asks so that bandwidth can be freed for analytics
- Work closely with cross-functional teams, including software developers, data engineers, and business analysts.
Requirements:
- Bachelors or Masters in a quantitative field (such as Engineering, Statistics, Math, Economics, or Computer Science with Modeling/Data Science)
- Ability to program in any high level language is required. Familiarity with R and statistical are a plus.
- Proven problem-solving and debugging skills.
- In-depth knowledge of machine learning algorithms and AI techniques.
- Ability to creatively apply AI solutions to real-world business problems.
- Familiarity with computer vision libraries and frameworks (e.g., OpenCV, TensorFlow, PyTorch, Transformers, NLTK).
- Experience with text analytics, data mining and social media analytics.
- Statistical knowledge in standard techniques: Logistic Regression, Classification models, Cluster Analysis, Neural Networks, Random Forests, Ensembles, etc.