Senior AI/ML Engineer - GenAI & Cloud Solutions

Role: 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. Futran Tech Solutions Pvt. Ltd.