AI/ML Architect

Role:- AI/ML Architect
Location:- Woodland Hills, CA
Duration: 24 Months

·      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.

Architectural Vision

·      Ability to define enterprise-level AI/ML architecture aligned with cloud-native principles.

·      Experience in scalability, resilience, and performance optimization.

Cloud & Data Expertise

·      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.

Leadership & Collaboration

·      Experience mentoring engineers, conducting reviews, and driving technical excellence.

·      Ability to collaborate with cross-functional teams including product, compliance, and operations.

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.