AI Software Engineer

CodesyncHR Services Inc.; United States; Remote; USD 120,000 - 160,000; Full time

Job summary

CodesyncHR Services Inc. is seeking a Software Engineer to design and deploy AI-enabled applications.

You will work with AI Engineers and product teams to move use cases from concept to MVP and production, focusing on architecture, scaffolding, enterprise integration, and cloud deployment. You will bring hands-on software engineering and cloud delivery experience, with familiarity in AI patterns, observability, and modern tools like GitHub Copilot and Claude Code to accelerate delivery.

Qualifications

• 5+ years of software engineering experience with production-grade apps.
• Experience introducing engineering best practices to AI teams.
• Experience delivering AI solutions with AI Engineers; patterns like RAG and agentic workflows.
• Strong Azure expertise across infra, security, identity and networking.
• DevOps tooling, CI/CD pipelines, environment management and secure deployments.
• Knowledge of relational, NoSQL, graph, vector and event-driven architectures.
• Proficiency with GitHub workflows and AI tools such as Copilot and Claude Code.

Responsibilities

• Architect and build scalable, secure AI solutions.
• Apply software engineering best practices for AI projects and systems.
• Lead code reviews and secure production readiness of services.
• Choose patterns for AI solutions with AI Engineers.
• Scaffold projects, repos, pipelines, environments and shared services.
• Build core components, APIs, data integrations and deployment-ready services.
• Collaborate with DevOps/platform teams on secure deployments and ops.

Skills

AI solution patterns

Architectural design

Cloud delivery

Team collaboration

DevOps practices

Tools

Azure

Neo4j

Cosmos DB

PostgreSQL

MongoDB

Kafka

Kubernetes

FastAPI

GitHub Copilot

Claude Code

Job description

About the role

We’re looking for a Software Engineer to build and deploy AI-enabled applications in partnership with AI Engineers and product teams. The role focuses on application architecture, project scaffolding, enterprise integration, cloud implementation and deployment readiness: everything needed to move AI use cases from proof of concept to MVP and into production.

You’ll bring strong software engineering and cloud delivery experience, with practical exposure to AI solutions and common AI architecture patterns. You don’t need deep model-development expertise, but you should understand how AI applications are structured, work well alongside AI Engineers, and use modern AI productivity tools such as GitHub Copilot and Claude Code in a disciplined way to speed up delivery.

What you’ll do

• Architect and build high-performance, scalable and secure AI solutions.
• Introduce and apply software engineering best practices: architecture and design patterns, scalability, performance and security.
• Own code reviews, and the scalability and security of systems in production.
• Choose the right technical patterns for AI solutions, together with AI Engineers.
• Scaffold projects, repositories, pipelines, environments and shared services.
• Build core application components, APIs, data integrations and deployment-ready services.
• Partner with DevOps and platform teams on secure, scalable and supportable deployments.
• Lead CI/CD, testing, release processes and operational readiness for MVP and production solutions.

What you’ll bring

• 5+ years of software engineering experience, with a hands-on builder mindset and a track record of production-grade applications.
• Experience introducing engineering best practices to AI teams and AI solutions.
• Practical experience on AI solutions alongside AI Engineers, including patterns such as RAG, agentic workflows, API-based model integration, and evaluation and observability.
• Strong Azure experience across infrastructure, platform services, security, identity and networking.
• Experience with DevOps tooling, CI/CD pipelines, environment management and secure deployment practices.
• Strong experience with technologies such as Neo4j, Cosmos DB, PostgreSQL, MongoDB, Kafka, containers, Kubernetes and FastAPI.
• A solid understanding of when to use relational, NoSQL, graph, vector and event-driven architectures.
• Proficiency with GitHub workflows, branching strategies and pull requests, and with AI tools such as GitHub Copilot and Claude Code.