Job description

Role: Senior AI/ML Engineer – GenAI & Cloud Solutions Location: Mason ,OH (onsite Role ) Key Responsibilities • Clienthitect 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: Clienthitect and oversee deployment of AI/ML workloads on Clienture Cloud, ensuring compliance, scalability, and cost optimization. • Data Clienthitecture: Design and optimize data pipelines and storage solutions using Clienture AI SeClienth, Redis, Cosmos DB, Blob Storage, and Iceberg. • Application Development: Build and manage Clienture Functions and Clienture Container Apps for microservices-based AI solutions. • Performance & Scalability: Define cloud-native Clienthitecture 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 & ReseClienth: 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 Clienture Cloud services, including deployment, monitoring, and scaling. • Databases: Expertise in Clienture AI SeClienth, Redis, Cosmos DB; familiarity with Blob Storage and Iceberg is advantageous. • Cloud-Native Clienthitecture: 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. Clienthitectural Vision • Ability to define enterprise-level AI/ML Clienthitecture aligned with cloud-native principles. • Experience in scalability, resilience, and performance optimization. 3. Cloud & Data Expertise • Hands-on deployment of AI workloads on Clienture Cloud. • Strong knowledge of databases, seClienth 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 & ReseClienth 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 Clienture Solutions Clienthitect or AI Engineering. • Publications, patents, or contributions to open-source AI/ML projects.

Requirements

Must-haves

Role: Senior AI/ML Engineer – GenAI & Cloud Solutions
Location: Mason ,OH (onsite Role )
Clienthitect 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: Clienthitect and oversee deployment of AI/ML workloads on Clienture Cloud, ensuring compliance, scalability, and cost optimization.
Data Clienthitecture: Design and optimize data pipelines and storage solutions using Clienture AI SeClienth, Redis, Cosmos DB, Blob Storage, and Iceberg.
Application Development: Build and manage Clienture Functions and Clienture Container Apps for microservices-based AI solutions.
Performance & Scalability: Define cloud-native Clienthitecture 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 & ReseClienth: 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).
Cloud Proficiency: Strong experience with Clienture Cloud services, including deployment, monitoring, and scaling.
Databases: Expertise in Clienture AI SeClienth, Redis, Cosmos DB; familiarity with Blob Storage and Iceberg is advantageous.
Cloud-Native Clienthitecture: 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)

Nice-to-haves

Programming: Advanced proficiency in Python; exposure to Java/Go is a plus.
Bachelors or master’s in computer science, AI/ML, or related field.
Certifications in Clienture Solutions Clienthitect or AI Engineering.
Publications, patents, or contributions to open-source AI/ML projects.

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