Health Care AI/ML Solutions Architect / Developer

Customer in Healthcare Industry looking for a Health Care AI/ML Solutions architect / Developer who will be instrumental in moving the company into AI / ML and Engineering. Responsibilities as follows:

• Recommend and develop comprehensive systems and frameworks for AI applications and products.
• Pitch concepts to team leadership and then lead construction of prototypes and minimum viable products to validate AI/ML solutions before committing substantial resources.
• Assist in designing and implementing the cloud architecture of large multi-faceted AI systems.
• Assist in the design and scaffolding of robust data pipelines that drive large complex AI systems.
• Develop specifications for low latency APIs and services necessary to deploy AI models and incorporate them into applications.
• Develop the code for monitoring models and AI systems that ensure consistent and reliable performance.
• Actively participate in a team that exercises principled, agile-like development practices
• Create and maintain thorough documentation that is consistent with team procedures, corporate policies, and expectations.
• Ensure peer review on all assigned work as well as conduct peer reviews over the work of others as requested.
• Guide AI engineers on AI/ML and industry best practices and methodologies
• Keep abreast of new tools and concepts through reading documentation or literature and actively practicing skills development.
• Advise team and division leadership on matters such as enterprise AI strategies, AI related technology strategies and roadmaps, AI related infrastructure strategies and roadmaps.
• Meet and collaborate with external stakeholders to conceptualize AI solutions that realize business value while ensuring AI governance adherence, AI best practices, and data quality.
• Provide thought leadership to the Premera AI community.
• Perform other duties as assigned.

Minimum Qualifications

• Bachelor's degree in Computer Science, Statistics, Mathematics, or a related field; or 2 years of experience in a related, professional IT/analytics position.
• Minimum of 5 years of industry experience in developing, deploying, and maintaining AI or ML systems. Up to two years of industry experience may be substituted with an AI centered master s/PhD degree or AI Engineering certifications.

Preferred Qualifications

• At least 6 years of experience in developing deep learning models using TensorFlow, PyTorch, MLX, JAX, or other modern deep learning frameworks. Previous experience with older libraries like Theano, or Caffe is also accepted. (May supplement with graduate level education or research experience).
• Experience with design patterns and microservices architecture, including at least 3 years with container orchestration in a production environment.
• Knowledge of and experience in implementing ethical AI practices, with at least 3 years spent working on projects that require explainable AI, fairness, and bias mitigation.
• At least 3 years expertise in implementing advanced techniques like Retrieval Augmented Generation, Tree of Thoughts, or Multimodal CoT in prompt engineering projects. Experience in leading prompt engineering teams, conducting code reviews, and setting best practices for prompt engineering within the organization is also essential.
• Minimum of 4 years of experience in successfully productionizing AI models, including constructing scalable data pipelines and establishing robust monitoring systems.
• At least 5 years working within Agile-like teams and environments

Knowledge, Skills, and Abilities

• Proven experience developing deep learning architectures such as CNNs, transformers, GANs, LSTMs, GNNs, Autoencoders, Diffusion Models, and Neural Ordinary Differential Equations (NODEs).
• Possess expertise in implementing advanced techniques like Retrieval Augmented Generation, Tree of Thoughts, or Multimodal CoT in prompt engineering projects. Experience in leading prompt engineering teams, conducting code reviews, and setting best practices for prompt engineering within the organization is also essential.
• Proven experience debugging AI systems and enhancing performance through hyperparameter tuning and similar techniques.
• Knowledgeable about software design patterns, microservices, distributed computing, container orchestration, and other relevant architectures.
• Adept software engineering skills as well as skills building secure, stable software systems at scale.
• Proven experience developing and optimizing ML solutions using languages like Python and libraries such as NumPy, Pandas, Matplotlib and scikit-learn.
• Familiarity working with traditional ML lifecycles.
• Familiarity with building minimal interfaces to interact with AI products, e.g. Streamlit or Shiny.
• Proficient at ethical AI practices including explainable AI, fairness, mitigation of bias/hallucinations.
• Strong communication, collaboration, and mentorship skills.
• Ability to articulate the technical details and tradeoffs of AI solutions to non-technical stakeholders in a clear and concise manner.