Qualifications

• This is not a traditional Data Architect or Cloud Architect role. We are seeking a technical leader with deep, hands-on Data Science expertise who also brings a strong end-to-end architectural vision for building scalable AI systems.

• Data Science DNA:

  • 7+ years of experience in Data Science with a proven track record of taking models from experimentation (e.g., Jupyter notebooks) into production-grade systems

• Architectural Mindset:

  • Strong understanding of scalable machine learning systems, MLOps practices, and lifecycle management

  • Hands-on experience with tools such as MLflow, Kubeflow, SageMaker, and cloud-native deployments on AWS, Azure, or GCP

• Deep Technical Stack:

  • Advanced proficiency in Python and common ML frameworks such as scikit-learn, TensorFlow, and PyTorch

  • Experience with Big Data technologies including Spark, Databricks, and distributed data processing

• Leadership & Communication:

  • Experience leading cross-functional teams and mentoring engineers

  • Ability to clearly communicate complex AI and ML concepts to non-technical stakeholders

Ideal Backgrounds

• AI/ML Engineers with strong experience in solution design and stakeholder management

• Solution Architects whose core expertise is Data Science and Machine Learning

Not a Fit For

• Traditional Solution Architects without hands-on Data Science experience

• Data Architects focused primarily on database schemas and storage systems

Responsibilities

Technical Leadership

• Serve as the primary bridge between Data Science and AI Architecture, translating clinical and business challenges into scalable, production-ready solutions

Solution Architecture

• Design end-to-end AI/ML ecosystems, including:

  • Data modeling and feature pipelines

  • Agentic workflows

  • Retrieval-Augmented Generation (RAG) pipelines

  • Large Language Model (LLM) orchestration and integration

Hands-on Development

• Lead by example through hands-on development in Python, R, and SQL

• Build and deploy production-ready models while guiding junior team members on engineering best practices, code quality, and system reliability

Governance, Security & Compliance

• Ensure all AI solutions are developed with a Privacy First mindset

• Maintain compliance with healthcare regulations (HIPAA) and broader data privacy and security standards

Strategic Collaboration

• Partner closely with engineering, product, and C-suite stakeholders to define AI strategy, roadmaps, and measurable success metrics

• Influence long-term platform and architectural decisions to support scalable AI adoption