Senior AI/ML Engineer – GenAI & Cloud Solutions

We are partnering with one of our Global Consulting client to fill a below position:

Job Title:                           Senior AI/ML Engineer – GenAI & Cloud Solutions

Location:                           Woodland hills, CA

Mode of Work:                Onsite

Duration:                          Contract Position

JOB DESCRIPTION:

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.