Key Responsibilities
- 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)
1. 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.
2. Architectural Vision
· Ability to define enterprise-level AI/ML architecture aligned with cloud-native principles.
· Experience in scalability, resilience, and performance optimization.
3. Cloud & Data Expertise
· Hands-on deployment of AI workloads on Azure Cloud.
· Strong knowledge of databases, search systems, and distributed storage.
4. Domain Knowledge
· Familiarity with healthcare regulations and ability to design compliant solutions.
5. Leadership & Collaboration
· Experience mentoring engineers, conducting reviews, and driving technical excellence.
· Ability to collaborate with cross-functional teams including product, compliance, and operations.
6. Innovation & Research Orientation
· 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.