Senior AI Engineer – Enterprise GenAI, On-Site in Nashville

Rōnin Consulting

Nashville (TN)

On-site

USD 140,000 - 190,000

Full time

39 hours ago

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Job summary

Rōnin Consulting seeks a Sr AI & Software Engineer in Nashville, TN, to lead production-grade Generative AI projects for a Fortune 500 healthcare client. You will architect RAG systems, grounding pipelines, and enterprise-grade full-stack apps on GCP Gemini, AWS Bedrock, or Azure AI Foundry platforms.

The role requires 8+ years in software engineering with hands-on enterprise AI delivery and on-site Nashville availability.

Qualifications

8+ years of software engineering experience with progression into AI/ML delivery.
Production delivery of Generative AI applications at enterprise scale.
Hands-on experience with enterprise AI platforms (GCP Vertex AI/Gemini, AWS Bedrock/SageMaker, or Azure AI Foundry/Azure OpenAI).
Deep experience designing and building RAG architectures in production.
Experience with vector stores and embedding pipelines (e.g., Vertex AI Vector Search, Pinecone, OpenSearch, Azure AI Search).
Data pipelines for embedding, grounding, and connecting LLMs with enterprise data sources.
Cloud-native development: serverless, Docker/Kubernetes, microservices on major hyperscalers.
Full-stack proficiency in 4+: Python, Java, C#, Node.js, SQL/NoSQL, ETL/data pipelines, queues, Docker/Kubernetes, microservices.

Responsibilities

Lead design, development, and deployment of scalable Generative AI solutions on enterprise cloud platforms (GCP Vertex AI/Gemini, AWS Bedrock/SageMaker, or Azure AI Foundry/Azure OpenAI).
Architect and build RAG systems grounding LLMs in proprietary data for regulated healthcare contexts.
Design data pipelines for embedding and vector stores with governance and compliance in mind.
Develop grounding strategies and data structures to connect LLMs with enterprise data sources.
Design agentic AI systems for multi-step tasks using frameworks like LangChain, LangGraph, Google ADK, or custom orchestration.
Integrate MCP for standardized LLM-to-tool communications where applicable.
Build evaluation frameworks, guardrails, and monitoring for model reliability and safety in production.
Conduct PoCs/PoT to secure stakeholder buy-in for AI initiatives.