Senior AI​/ML Engineer – GenAI & Cloud Solutions

Location:

Woodland hills, CA (onsite Role )

• 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.

• 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.

• 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.

• 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.

• 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.