AI/ML Architect (Solution)
Required Skills & QualificationsKey ResponsibilitiesArchitect 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 & ExpertiseAgentic 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 QualificationsBachelors 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.