AI​/ML Architect​/Lead C2C Mason ,OH

Job Title :

AI/ML Architect/ Lead

Location :
Mason ,OH (Onsite)

Duration : 12 Months

Job Description :

Looking for 12+ Years of Consultant.
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.

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.

• 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.

• Ability to define enterprise-level AI/ML architecture aligned with cloud-native principles.
• Experience in scalability, resilience, and performance optimization.

• Hands-on deployment of AI workloads on Azure Cloud.
• Strong knowledge of databases, search systems, and distributed storage.

• Familiarity with healthcare regulations and ability to design compliant solutions.

• Experience mentoring engineers, conducting reviews, and driving technical excellence.
• Ability to collaborate with cross-functional teams including product, compliance, and operations.

• Evidence of staying current with GenAI advancements and applying them to real-world problems.