Sr Architect

Job Summary

This hybrid role for a Senior Architect with 22 to 25 years of experience focuses on designing and delivering scalable AI and machine learning solutions using hands-on AI architect/strategist. Someone who can define AI assurance strategy work with their CoE and business and Translate use cases into execution. The role emphasizes robust architecture operational reliability and responsible AI to support provider domain initiatives with high business impact.

Responsibilities

• Design and implement end to end AI architectures that use MLFlow and Python to deliver robust and scalable machine learning solutions for enterprise grade applications
• Develop and maintain standardized workflows for experiment tracking and model lifecycle management using MLFlow to improve transparency and reproducibility across projects
• Build and optimize high performance application programming interfaces using FastAPI to expose machine learning models as reliable services for internal and external consumers
• Create secure and efficient model serving strategies that address latency scalability and observability requirements while aligning with organizational risk and compliance expectations
• Design evaluation frameworks using OpenAI playground and Promptflow to systematically test prompts models and configurations for accuracy safety and user experience
• Guide cross functional teams in adopting best practices for prompt engineering and experimentation so that AI features provide consistent and explainable outcomes for business users
• Develop reusable Python libraries templates and reference implementations that accelerate delivery of AI solutions and reduce technical debt across multiple initiatives
• Collaborate closely with product engineering and operations partners to translate complex provider domain needs into clear technical designs and implementation roadmaps
• Establish monitoring alerting and observability standards for model serving and FastAPI services to ensure high availability and rapid issue detection in production environments
• Drive continuous improvement in data quality feature engineering and deployment pipelines by applying systematic root cause analysis and evidence based decision making
• Align architectural decisions with organizational goals by documenting tradeoffs constraints and future evolution paths so that stakeholders can plan investments responsibly
• Mentor engineering teams on advanced topics such as MLFlow usage model governance responsible AI and secure coding so that capabilities scale sustainably over time
• Partner with security and compliance specialists to design architectures that safeguard data privacy reduce bias risk and meet regulatory expectations in the provider focused solutions
  
  

• Display deep experience in Python MLFlow and end to end machine learning solution design gained through long term work in complex enterprise environments
• Show proven expertise in building and operating APIs with FastAPI including performance optimization testing automation and observability practices
• Demonstrate hands on experience with model serving frameworks and deployment patterns covering containerization scalability patterns and rollback strategies
• Exhibit practical familiarity with OpenAI playground and Promptflow for designing evaluating and managing prompt based and generative AI solutions
• Illustrate understanding of provider domain processes terminology and workflows so that AI solutions align well with practical healthcare or related use cases
• Show experience working in hybrid work models and collaborating effectively with distributed teams using modern communication and planning tools
• Highlight strong communication skills for explaining complex AI architecture choices to both technical and nontechnical stakeholders in clear and concise ways
• Display commitment to responsible AI including fairness transparency and governance practices that align with organizational standards and societal expectations