Architect and Design: Lead the design of scalable, secure, and high-performance AI/ML systems leveraging Agentic Layer A2A frameworks and MCP Protocols.
Solution Engineering: Drive end-to-end solution development including vector embeddings, prompt engineering, and context engineering for enterprise-grade GenAI applications.
Cloud Deployment: Architect and oversee deployment of AI/ML workloads on Azure Cloud, ensuring compliance, scalability, and cost optimization.
Data Architecture: Design and optimize data pipelines and storage solutions using Azure AI Search, Redis, Cosmos DB, Blob Storage, and Iceberg.
Application Development: Build and manage Azure Functions and Azure Container Apps for microservices-based AI solutions.
Performance & Scalability: Define cloud-native architecture patterns, implement performance tuning, and ensure resilience across distributed systems.
Domain Expertise: Apply deep knowledge of healthcare domain requirements, ensuring solutions meet regulatory standards (HIPAA, GDPR, etc.) and handle sensitive data securely.
Technical Leadership: Mentor engineering teams, establish best practices, and conduct design/code reviews.
Innovation & Research: Stay ahead of emerging GenAI, LLM/NLM trends, and integrate cutting-edge approaches into enterprise solutions.
Required Skills & Expertise
Agentic Layer & Protocols: Hands‑on expertise with Agentic Layer A2A frameworks and MCP Protocol for multi‑agent orchestration.
AI/ML Engineering: Strong background in vector embeddings, prompt engineering, context engineering, and fine‑tuning LLMs.
GenAI & LLM Concepts: Deep understanding of Generative AI, Natural Language Models (NLM), and Large Language Models (LLM).
Programming: Advanced proficiency in Python; exposure to Java/Go is a plus.
Cloud Proficiency: Strong experience with Azure Cloud services, including deployment, monitoring, and scaling.
Databases: Expertise in Azure AI Search, Redis, Cosmos DB; familiarity with Blob Storage and Iceberg is advantageous.
Cloud‑Native Architecture: Solid grasp of microservices, containerization, serverless computing, scalability, and performance optimization.
Healthcare Domain: Experience working with regulated data environments and compliance frameworks.
Evaluation Criteria (Critical Components)
Technical Depth
Ability to design and implement multi‑agent AI systems.
Experience in LLM fine‑tuning, embeddings, and context engineering.
Expertise in coding proficiency with production‑grade systems in Python.
Ability to define enterprise‑level AI/ML architecture aligned with cloud‑native principles.
Experience in scalability, resilience, and performance optimization.
Hands‑on deployment of AI workloads on Azure Cloud.
Strong knowledge of databases, search systems, and distributed storage.
Domain Knowledge
Familiarity with healthcare regulations and ability to design compliant solutions.
Experience mentoring engineers, conducting reviews, and driving technical excellence.
Ability to collaborate with cross‑functional teams including product, compliance, and operations.
Evidence of staying current with GenAI advancements and applying them to real‑world problems.
Preferred Qualifications
Bachelors or master’s in computer science, AI/ML, or related field.
Certifications in Azure Solutions Architect or AI Engineering.
Publications, patents, or contributions to open‑source AI/ML projects.