Role:

GenAI & Cloud Solutions Location:

Mason, OH -

Hybrid Job Type:

Contract/fulltime 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 Gen

AI, 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. Gen

AI & 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): 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. Architectural Vision Ability to define enterprise-level AI/ML architecture aligned with cloud-native principles. Experience in scalability, resilience, and performance optimization. Cloud & Data Expertise Hands-on deployment of AI workloads on Azure Cloud. Strong knowledge of databases, search systems, and distributed storage. Domain Knowledge Familiarity with healthcare regulations and ability to design compliant solutions. Leadership & Collaboration Experience mentoring engineers, conducting reviews, and driving technical excellence. Ability to collaborate with cross-functional teams including product, compliance, and operations. 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. 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