Senior AIML Engineer

Role: Senior AI/ML Engineer GenAI & Cloud Solutions

Location: Woodland hills CA (onsite Role )

looking minimum 15 years experience.

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 masters 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.
  
  Thanks and Regards
  Aaksh