Enterprise Solution Architect

Role Overview

• Serve as the premier technical authority driving the enterprise-wide architecture, engineering, and deployment of Agentic AI and Generative AI platforms.
• Architect scalable, fault-tolerant enterprise platforms for autonomous, multi-agent systems, moving beyond isolated models to comprehensive reasoning engines.
• Design the underlying infrastructure for agent state management, memory, orchestration, and tool utilization using modern frameworks (e.g., AutoGen, LangGraph).
• Bridge the gap between AI science and software engineering, establishing the technical blueprints for integrating advanced RAG, Graph RAG, and LLMs into high-concurrency production environments.
• Serve as the primary technical advisor to C-suite stakeholders, product leadership, and external healthcare clients, translating business requirements into actionable AI roadmaps.
• Lead technical discussions with customers to build trust in our AI architecture, addressing concerns related to explainability, system latency, and clinical safety.
• Drive cross-functional alignment, ensuring product, engineering, and data science teams are executing against a unified architectural vision.
• Maintain deep technical oversight over traditional ML, Deep Learning, and Generative AI pipelines to ensure the right tool is utilized for the right problem.
• Oversee the design of robust data ingestion pipelines capable of handling highly complex, multi-modal healthcare data (FHIR, structured records, complex PDFs) for agentic processing.
• Lead initiatives to optimize model serving, inference latency, and computational cost across distributed cloud architectures.
• Establish enterprise-wide engineering standards for AI development, including code quality, containerization, CI/CD for ML, and comprehensive system telemetry.
• Architect "security-by-design" AI systems, ensuring strict adherence to healthcare privacy regulations (HIPAA, HITRUST) and implementing guardrails against model drift and hallucinations.

Requirements

• Master's degree in computer science, AI, Software Engineering, or related field AND 10+ years of professional experience in software engineering, ML/AI architecture, and distributed systems OR PhD in Computer Science, AI, or related field AND 8 years of experience.
• 3 years of hands-on expertise in building and deploying Agentic workflows and orchestration frameworks (e.g., AutoGen, LangChain) in production environments.
• 5 years of experience in both classical Machine Learning/Deep Learning and modern Generative AI paradigms.
• 5 years of experience architecting scalable backend systems, APIs, and ML infrastructure using Python and cloud-native technologies (AWS/Azure/GCP).
• Demonstrated track record of successful client-facing or executive stakeholder management, with the ability to explain complex architectural concepts to non-technical audiences.
• Expertise in advanced retrieval systems, including Graph RAG and complex document intelligence.
• Extensive experience in system design patterns, microservices architecture, and infrastructure as code.
• Prior experience acting as a Chief Architect or equivalent senior technical leadership role within the healthcare or life sciences sector.
• Deep understanding of MLOps practices, model evaluation, telemetry, and continuous deployment of autonomous systems.

Tech Stack

• AWS
• Azure
• Cloud
• Distributed Systems
• Google Cloud Platform
• Microservices
• Python

Benefits

• Medical, Dental & Vision
• Health Savings Accounts
• Health Care & Dependent Care Flexible Spending Accounts
• Disability Benefits
• Life Insurance
• Voluntary Benefits
• Paid Absences
• Retirement Benefits