AI Solution Engineer

Experis Technology Group; Jacksonville (FL); Hybrid; USD 150,000 - 190,000; Full time

Job summary

Experis Technology Group seeks an AI Solution Engineer with 8+ years of experience to design, build, and deploy enterprise-grade AI applications from concept to production. This hands-on role spans frontend, backend, AI, cloud, and data platforms, partnering with stakeholders to translate requirements into secure products.

You will own the full lifecycle, architect scalable solutions, and lead demos with product owners to ensure responsible AI and compliance throughout.

Qualifications

• 8+ years delivering enterprise AI products from concept to production.
• Experience with healthcare or regulated industries preferred.
• Strong understanding of AI/ML model lifecycles and governance.

Responsibilities

• Design, build, and deploy end-to-end AI-powered enterprise applications.
• Create scalable, secure architectures and data pipelines.
• Build solutions with LLMs, RAG, AI agents, and prompt engineering.
• Develop multi-agent AI workflows with agent frameworks and tool calling.
• Implement frontends with React/TypeScript/Vite/Tailwind.
• Develop backend services and REST APIs using Python frameworks.
• Integrate AI with enterprise systems and data stores.
• Design Azure Data Lake, ETL/ELT pipelines, Databricks workflows.
• Deploy cloud-native apps with Azure, Docker, Kubernetes, CI/CD.
• Implement secure authentication (Entra ID, OAuth2, JWT, SAML).
• Establish evaluation frameworks and testing, monitoring, compliance.

Skills

AI/ML

React

TypeScript

Python

FastAPI

Flask

Azure

Databricks

Docker

Kubernetes

CI/CD

OAuth2

JWT

Security

Education

Bachelor's degree or higher in CS/Related field

Tools

React

Vite

Tailwind CSS

FastAPI

Flask

Azure

Databricks

Docker

Kubernetes

Azure DevOps

Job description

AI Solution Engineer

Hybrid - must be onsite in NYC every Tuesday

9-12 months

We are looking for an 8+ years of experience AI Solution Engineer to design, build, and deploy enterprise-grade AI applications from concept to production. This hands-on engineering role spans frontend, backend, AI, cloud, and data platforms. The successful candidate will be partnering with business stakeholders, end users, and product owners to turn requirements into secure, production-ready AI products.

The ideal candidate can gather requirements, design scalable architectures, develop modern React applications, build backend APIs, implement AI capabilities, engineer data platforms, and deploy secure cloud-native applications. They take ownership from concept through production deployment, monitoring, and continuous improvement.

Key Responsibilities

• Design, build, and deploy end-to-end AI-powered enterprise applications.
• Create scalable, secure, and maintainable solution architectures.
• Build solutions with LLMs, Retrieval-Augmented Generation (RAG), AI agents, prompt engineering.
• Develop and orchestrate multi-agent AI workflows using modern agent frameworks and tool calling.
• Must have Built modern frontends with React, TypeScript, Vite, and Tailwind CSS.
• Develop backend services and REST APIs with Python, FastAPI, and Flask.
• Integrate AI applications with enterprise systems, APIs, databases, authentication providers, and business applications.
• Design and implement Azure Data Lake, ETL/ELT pipelines, Spark/Databricks workflows, and Lakehouse architectures.
• Deploy and operate cloud-native applications with Azure, Docker, Kubernetes, and CI/CD pipelines.
• Implement secure authentication with Microsoft Entra ID, OAuth2, JWT, or SAML.
• Establish AI evaluation frameworks, including regression tests, benchmark datasets, human review loops, and acceptance criteria.
• Optimize AI applications for latency, scalability, reliability, and cost.
• Write unit, integration, and end-to-end tests.
• Partner with end users, product owners, and the Lead Product Engineer to gather requirements, run demos, and incorporate feedback.
• Ensure Responsible AI, governance, security, compliance, monitoring, and observability.

Product End-to-End Delivery

• Own the full AI product lifecycle, from ideation to production support.
• Define technical architecture, roadmaps, milestones, and release strategies.
• Translate business requirements into production-ready AI solutions.
• Build frontends, backends, AI services, APIs, and data pipelines.
• Manage production releases, monitoring, and continuous improvement.
• Create technical documentation and mentor engineers.

Required Technical Skills

• AI/ML: LLMs, prompt engineering, RAG, AI agents, MCP, function calling, semantic search, vector databases, LLM evaluation, and guardrails.
• Programming: Python, SQL, JavaScript, and TypeScript.
• Frontend: React, Vite, Tailwind CSS, HTML5, CSS3
• Backend: FastAPI, Flask, REST APIs, OpenAPI, Gemma preferred.
• Data Engineering: Azure Data Lake, Azure Data Factory, Databricks, Spark, Delta Lake, ETL/ELT, and lakehouse architecture.
• Cloud: Azure OpenAI, Azure AI Foundry, Azure AI Search, AKS, Azure Functions, Azure App Service, Cosmos DB, Azure SQL, Key Vault, and Azure API Management.
• DevOps: Git, GitHub, GitHub Actions, Azure DevOps, Docker, Kubernetes, Helm, and CI/CD.
• Security: Microsoft Entra ID, OAuth2, JWT, and secrets management.

Preferred Qualifications

• 8+ Proven experience delivering enterprise AI products from concept to production.
• Experience in healthcare or another regulated industry.
• Experience with HL7 v2, FHIR R4/R5, OMOP CDM, and Epic/Epic Clarity. (Optional)
• Experience with Model Context Protocol (MCP)
• Strong communication, stakeholder management, and Agile/Scrum delivery skills.