Machine Learning Software Engineer

• Identify and research areas that can be automated in the business process and begin to build components that perform the automation utilizing the latest AI and ML models and technology to allow for rapid prototyping and building of the application 
• Research different AI tools and which are best to be utilized in the context of a large set of external databases combined with internal databases hosted by proprietary platforms with APIs
• Research the APIs necessary to access all the various diverse sources of business records
• Develop automation to message prospects via various communication channels, process the responses, identify next steps based on the responses, and automate the next steps
• Develop a database that will store all the information necessary to manage and track the automation
• Identify addition resources necessary to build the full application, including the user interface and back end infrastructure based on cloud technologies
• Help build the architecture for the overall solution

Skill Sets Required

• Machine Learning and Artificial Intelligence: Proficiency in developing and implementing ML models and deep learning neural networks. Understanding of various algorithms and frameworks - skill sets required: TensorFlow, PyTorch, scikit-learn.
• Programming and Software Development: Build applications that leverage the ML & AI tools to produce results. Skill sets required: Python, JavaScript, optionally Java or C++ for building high performance components.  
• Natural Language Processing (NLP): Depending on the focus of the role, expertise in NLP (for text-based AI). Familiarity with relevant libraries and frameworks like spaCy, NLTK, and transformers like BERT or GPT. 
• Data Engineering: Strong skills in data preprocessing, data cleansing, and managing large volumes of data. Experience with tools like SQL, NoSQL, ETL, Hadoop/Spark. 
• Model Training: Expertise in training and fine-tuning ML models on large datasets. Knowledge of hyperparameter tuning and optimization techniques.
• Cloud Services: Familiarity with cloud platforms such as AWS, Azure, or Google Cloud for deploying and scaling AI applications.

Education Requirements

• Bachelors or Masters in Computer Science or related field