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
- 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
- Implement secure authentication with Microsoft Entra ID, OAuth2, JWT, or SAML.
- 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.
- Frontend: React, Vite, Tailwind CSS, HTML5, CSS3
- ETL/ELT, and lakehouse architecture.
- 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.