Generative AI Engineer

Veridic Solutions; Mason (OH); On-site; USD 140,000 - 230,000; Full time

Job summary

Veridic Solutions in the United States is seeking a senior AI/ML engineer to design and implement scalable multi-agent AI systems, focusing on agentic layers and MCP protocols.

You will fine-tune LLMs, manage embeddings and context engineering, and build cloud-native architectures on Azure to deploy production-grade AI workloads with an emphasis on healthcare data compliance and secure, scalable solutions.

Qualifications

• Experience designing and implementing multi-agent AI systems.
• Proficiency in LLM fine-tuning, embeddings, and context engineering.
• Production-grade Python development for AI workloads.
• Experience deploying AI workloads on Azure.
• Knowledge of scalable, cloud-native architectures.
• Experience with regulated data environments and healthcare compliance.

Skills

Agentic Layer & Protocols

AI/ML Engineering

GenAI & LLM Concepts

Python Programming

Azure Cloud

Cosmos DB

Redis

Azure AI Search

Cloud-Native Architecture

Healthcare Compliance

Job description

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)

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.