Enterprise Solution Architect

Solventum

Enterprise Agentic Solution Architect specializing in architecting and deploying AI solutions in healthcare. Driving enterprise-wide architecture while engaging with C-suite stakeholders and healthcare clients.

Posted 6/2/2026full-timeRemote • Texas • 🇺🇸 United StatesSeniorLead💰 $197,600 - $271,700 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformMicroservicesPython

About the role

Key responsibilities & impact

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

What you’ll need

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.

Benefits

Comp & perks

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

ATS Keywords

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Hard Skills & Tools

Agentic AIGenerative AIMachine LearningDeep LearningPythonCloud-native technologiesMLOpsData ingestion pipelinesArchitecting scalable systemsSystem design patterns

Soft Skills

Stakeholder managementTechnical advisoryCross-functional alignmentClient-facing communicationTrust buildingExplainabilityLeadershipCollaborationProblem-solvingTechnical discussions