Artificial Intelligence​/Machine Learning Developer

We are currently seeking a capable AI Developer who wants to make a difference in the lives of our members. The successful candidate is responsible for designing, developing, and implementing artificial intelligence solutions, with a strong focus on large language models (LLMs) and their integration into CHG's .NET ecosystem. This role works on various stages of the AI/ML development life cycle, from data preprocessing and model development to deployment and monitoring.

The AI developer is generally focused on integrating and implementing AI/ML algorithms and logic into the deliverables of an IT project. This role requires a blend of technical expertise, problem-solving skills, and collaboration with cross-functional teams to deliver AI-driven solutions aligned with organizational goals. Since our work directly effects our customer service to our members, we look for talented individuals who will create a high-quality experience.

Works closely with all departments necessary to ensure that the processes, programs and services are accomplished in a timely and efficient manner in accordance with CHG policies and procedures and in compliance with applicable state and federal regulations including DHCS and CMS.

• Collaborates with various stakeholders to understand, meet, and answer business challenges using AI software.
• Develops and implements machine learning models and algorithms to solve complex problems and extract insights from large datasets.
• Selects appropriate machine learning techniques and models based on the problem at hand, such as supervised learning, unsupervised learning, or reinforcement learning.
• Trains, validates, and fine-tunes models using various algorithms and frameworks, such as Tensor Flow and PyTorch.
• Optimizes models for performance, scalability, and accuracy, considering factors like computational resources and memory constraints.
• Collaborates with software developers to integrate AI/ML models into existing systems or develop new applications.
• Stays updated with the latest advancements in AI/ML technologies and contributes to the adoption of best practices within the team.
• Produces clear documentation, ensure AI solutions meet compliance, security, and ethical standards.

Minimum of 3 years of software design, development and implementation using the following technologies:

• Proficiency in C# and the .NET ecosystem, with experience using or integrating Python-based AI models into .NET applications.
• Experience designing and consuming RESTful APIs
• Hands‑on experience with Azure AI Services and or OpenAI APl's
• Strong understanding of machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and deep learning; familiarity with natural language processing (NLP) techniques and libraries for text analysis and sentiment analysis.
• Considerable knowledge of machine learning libraries and frameworks such as Tensor Flow, PyTorch, scikit-learn, or Keras.
• Hands‑on experience with LLM frameworks: Hugging Face, Lang Chain, LoRA, PEFT, or related.
• Deep understanding of transformer architectures, NLP techniques, and embeddings.
• Hands‑on experience with vector databases
• Proficient in exploratory data analysis (EDA) and data visualization using libraries like Pandas, Matplotlib, or Seaborn.
• Strong knowledge of data structures, algorithms, and model evaluation techniques.
• Hands‑on experience with cloud platforms such as AWS, Azure, and GCP.
• Experience with version control systems like Git for collaborative development and code management.
• Familiarity with CI/CD pipelines, APis, microservices, and system integrations.
• Strong analytical and problem‑solving skills, with the ability to analyze complex issues and design innovative AI/ML.
• Effective communication and collaboration skills to work with cross‑functional teams.
• Ability to manage multiple priorities in a fast‑paced environment.
• Demonstrated curiosity, adaptability, and passion for innovation in AI technologies.
• Experience with LLMOps practices, including monitoring for hallucinations, bias, and compliance in deployed models.