AI Solution Engineer

Job Summary

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, product owners to turn requirements into

secure, production-ready AI products.

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.

 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.

Candidate Profile

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.