AI Machine Learning Engineer

Position Summary :

As an AI Machine Learning Engineer you are responsible for developing AI capable of mimicking human thought processes as closely as possible so that they can function reliably and effectively. You will be responsible for designing, developing, and managing artificial intelligence (AI) systems that can autonomously “generate” content, such as text and images. You develop and leverage deep learning and machine learning techniques to train AI models on large datasets, enabling these models to generate new, original content that mimics human-like creativity, using your expertise in algorithm optimization, data modeling, to apply AI to solve complex, creative challenges.

Essential functions of the job include but are not limited to :

Qualifications :

• Evaluating, developing, and testing Artificial Intelligence / Machine Learning applications and solutions.
• Developing and optimizing AI / ML algorithm algorithms for Precision products across all business lines.
• Analyzing data gathered during tests to identify strengths and weaknesses of AI and iterating to improve overall AI / ML algorithms.
• Troubleshooting and addressing problems with deployed AI to improve user experience.
• Solving operational, quality, and engineering problems using big data and other data sets including healthcare claims data.
• Interacting with internal customers to drive validation trials, implement process improvements and writing documentation to ensure high quality AI / ML output across such applications.
• Remaining up to date on the latest innovations in machine learning while building knowledge base across Precision AQ teams.
Minimum Required :
• BA Degree
• Minimum of 5 years experience in
• Advanced programming knowledge, including mastery of programming languages such as Python, and especially AI-centric libraries like TensorFlow, PyTorch, and Keras. This includes the ability to implement and manipulate complex algorithms fundamental to developing generative AI models.
• Expertise in generative models such as generative adversarial networks (GANs) and variational autoencoders (VAEs). The candidate should be able to design, train, and optimize these models to generate high-quality, creative content.
• Natural language processing (NLP) for text generation projects. This includes familiarity with techniques for text parsing, sentiment analysis, and the use of transformers like GPT (generative pre-trained transformer) models.
• Data management knowledge, including data pre-processing, augmentation, and generation of synthetic data. This involves cleaning, labeling, and augmenting data to train and improve AI models.
• Cloud computing and deployment knowledge for deploying and managing AI applications on cloud platforms like AWS, Google Cloud, or Microsoft Azure. This includes understanding containerization technologies like Docker and orchestration tools like Kubernetes, which are important for scaling AI solutions.

Skills :

Competencies :