AI Solution Architect

Job Description

The AI Solution Architect will design and lead the implementation of scalable, secure, and responsible AI/ML and Generative AI solutions for healthcare organizations. The role requires a strong understanding of healthcare workflows, data, interoperability, cloud architecture, and AI technologies.

Key Responsibilities

• Define AI and GenAI architecture for healthcare applications and enterprise use cases.
• Partner with healthcare business, clinical, IT, data, security, and compliance stakeholders to identify and prioritize AI opportunities.
• Translate business and clinical requirements into scalable technical architectures and solution designs.
• Design AI solutions using LLMs, RAG, NLP, machine learning, computer vision, and intelligent automation where appropriate.
• Architect secure data pipelines for structured and unstructured healthcare data.
• Work with healthcare standards and interoperability technologies such as HL7, FHIR, DICOM, and APIs.
• Design solutions integrating EHR/EMR platforms, healthcare data warehouses, data lakes, and external systems.
• Develop architectures for LLM applications, AI agents, prompt engineering, vector databases, embeddings, and RAG pipelines.
• Establish model evaluation, monitoring, observability, governance, and lifecycle-management frameworks.
• Ensure AI solutions address privacy, security, compliance, explainability, bias, and responsible AI requirements.
• Collaborate with data scientists, ML engineers, software engineers, DevOps engineers, and clinical SMEs.
• Define cloud architecture and technology strategy across AWS, Microsoft Azure, or Google Cloud.
• Develop architecture diagrams, technical specifications, proof-of-concepts, and implementation roadmaps.
• Evaluate AI platforms, models, APIs, and third-party technologies.
• Establish reusable AI/ML architecture patterns and enterprise standards.
• Optimize AI solutions for scalability, reliability, latency, and cost.
• Support production deployment and troubleshooting of AI solutions.
• Mentor engineering and development teams on AI architecture and best practices.

Required Skills & Qualifications

• Strong experience in AI/ML architecture and Generative AI.
• Hands-on understanding of LLMs, RAG, NLP, embeddings, vector databases, AI agents, and prompt engineering.
• Experience with Python and modern AI/ML frameworks.
• Strong understanding of cloud architecture, preferably Azure/AWS/GCP.
• Experience with APIs, microservices, containers, Kubernetes, and event-driven architectures.
• Knowledge of data engineering, data lakes, data warehouses, ETL/ELT, and real-time data pipelines.
• Experience with healthcare interoperability standards such as FHIR, HL7, and DICOM.
• Understanding of EHR/EMR ecosystems and healthcare data models.
• Experience with SQL, NoSQL, and vector databases.
• Knowledge of MLOps/LLMOps, CI/CD, model monitoring, and AI observability.
• Strong understanding of cybersecurity, identity management, encryption, and secure API design.
• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Healthcare Informatics, Engineering, or a related field.

What We Offer

• Opportunity to work on enterprise technology projects.
• Collaborative and professional work environment.
• Career growth and learning opportunities.
• Exposure to modern technology platforms and solutions.
• Competitive compensation based on experience and expertise.