Urgent Hiring --Senior AI/ML Engineer(12+ Years) – GenAI & Cloud Solutions ----Woodland hills, CA (onsite Role ) -H1B Need

[krish alpha]

Hi,

Hope you are doing well.

This side Krishan Kumar from Alpha silicon. If you are interested in this role, please let me know.

Role : Senior AI/ML Engineer – GenAI & Cloud Solutions

Location : Woodland hills, CA (onsite Role )

Term:- Contract 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