Senior AI/ML Engineer – GenAI & Cloud Solutions

Location: Woodland hills, CA (onsite Role ) 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. Show more Show less