Join a global organization dedicated to improving health outcomes through technology. We connect people with the care, pharmacy benefits, data, and resources they need to thrive. Our inclusive culture, talented colleagues, comprehensive benefits, and career development opportunities empower you to make a significant impact. Come, be a part of advancing health optimization worldwide.

We are seeking an Architect to design, build, and maintain scalable machine learning and Generative AI solutions. This role involves developing predictive models, NLP solutions, deep learning applications, and LLM-based systems to address complex business challenges. You will also be responsible for optimizing data pipelines, feature engineering, and model training frameworks using extensive healthcare and business datasets.

This position requires hands-on experience with RAG architectures, vector search, prompt engineering, and LLM evaluation. You will implement MLOps and LLMOps best practices, including CI/CD, automated testing, model deployment, monitoring, and observability. Collaboration with cross-functional teams, adherence to Responsible AI principles, data governance, HIPAA, and enterprise security standards are key. Continuous improvement of model performance, latency, cost, and user experience through experimentation is expected. Additionally, you will provide technical leadership, mentor junior engineers, and evaluate emerging AI technologies.

Design, deploy, and maintain scalable machine learning and Generative AI solutions for clinical, pharmacy, payer, and operational use cases. Develop predictive models, recommendation systems, NLP, deep learning, and LLM applications. Build and optimize data pipelines, feature engineering, and model training using large healthcare and business datasets. Implement RAG architectures, vector search, prompt engineering, and LLM evaluation frameworks. Fine-tune and deploy foundation models, ensuring reliability, scalability, security, and performance. Implement MLOps/LLMOps best practices, including CI/CD, testing, deployment, monitoring, and observability. Collaborate with stakeholders to translate requirements into scalable AI solutions. Ensure adherence to Responsible AI, data governance, HIPAA, and security standards. Analyze model performance and optimize for accuracy, latency, cost, and user experience. Provide technical leadership, mentor engineers, conduct code reviews, and contribute to best practices. Evaluate new AI technologies and recommend innovative approaches. Support production AI systems through monitoring, issue resolution, and continuous improvement.

A Bachelor's degree in Computer Science, Engineering, Data Science, Mathematics, or a related technical field is required.

Possess at least 5 years of professional experience in software engineering, machine learning engineering, or AI development. Requires 3+ years of experience building and deploying machine learning solutions in production environments.

Demonstrate hands-on experience with Generative AI technologies, including Large Language Models (LLMs), embeddings, prompt engineering, RAG, and vector databases. Experience developing cloud-native solutions on AWS, Azure, or Google Cloud Platform is essential. Familiarity with containerization and orchestration technologies like Docker and Kubernetes is necessary.

Knowledge of MLOps practices, including model deployment, monitoring, experiment tracking, and CI/CD automation, is required. Possess a solid understanding of machine learning fundamentals, such as supervised and unsupervised learning, statistical modeling, and model evaluation.

Solid proficiency in Python and machine learning frameworks like PyTorch, TensorFlow, or Scikit-Learn is essential. Demonstrated expertise in SQL, data modeling, and large-scale data processing technologies is required. Proven ability to communicate complex technical concepts effectively to both technical and non-technical audiences.