AI Software Engineer

TechDoQuest; New York (NY); On-site; USD 150,000 - 210,000; Full time

Job summary

TechDoQuest is seeking an 8+ years AI Solution Engineer to design, build, and deploy enterprise-grade AI applications from concept to production. This hands-on role covers frontend, backend, AI, cloud, and data platforms.

The candidate will partner with stakeholders and product owners to turn requirements into secure, production-ready AI products, with emphasis on Azure, Docker, Kubernetes, and CI/CD pipelines.

Qualifications

• 8+ years delivering enterprise AI products.
• Experience building end-to-end AI-powered apps.
• Strong skills in LLMs, RAG, AI agents, and secure production delivery.
• Hands-on with Azure data lake, ETL/ELT, Spark/Databricks.

Responsibilities

• Design, build, and deploy end-to-end AI-powered enterprise applications.
• Create scalable, secure, and maintainable solution architectures.
• Develop frontend, backend, AI services, and data pipelines.
• Partner with stakeholders to translate requirements into production-ready AI products.
• Own full AI product lifecycle from ideation to production support.
• Implement security, governance, monitoring, and observability.

Skills

LLMs

Prompt engineering

RAG

AI agents

Semantic search

Frontend React

TypeScript

Vite

Tailwind CSS

Python

FastAPI

Flask

Azure

Docker

Kubernetes

CI/CD

OAuth2

JWT

Security

Tools

Azure Databricks

Spark

Cosmos DB

Azure SQL

Key Vault

Azure API Management

GitHub Actions

Docker

Kubernetes

Helm

Job description

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.