Junior AI .NET Engineer

EPAM Systems; United States; Remote; USD 65,000 - 95,000; Full time

Job summary

EPAM Systems seeks a Junior AI .NET Engineer to build robust backend services, APIs, and modern web apps while applying generative AI tools to speed development. You will join an agile team with senior engineers, bridging traditional enterprise backend with cutting-edge AI tech.

Work with C#, ASP.NET Web API, EF Core, and SQL to deliver scalable solutions and intelligent features. Collaboration, code reviews, and cloud–driven projects are part of daily routines.

Qualifications

• 1+ years with C# and the .NET ecosystem
• Experience designing RESTful APIs and ASP.NET Web API
• Proficiency with SQL and relational databases
• Familiarity with Agile methodologies and Git workflows
• English proficiency (B2+)

Responsibilities

• Design, develop, and maintain scalable backend services and RESTful endpoints using C# and ASP.NET Web API
• Implement data access with Entity Framework Core and SQL databases
• Leverage generative AI tools to accelerate coding, debugging, and testing
• Participate in Agile ceremonies and code reviews to deliver increments
• Support full-stack features with frontend tech and cloud services
• Experiment with AI agents and AI-native engineering patterns

Skills

C#

ASP.NET

REST APIs

SQL

Git workflows

Agile

English (B2+)

Tools

Git

Visual Studio

Entity Framework Core

Job description

We are seeking a Junior AI .NET Engineer to build robust backend services, APIs, and modern web applications while leveraging generative AI tools and workflows to accelerate development and build intelligent features. Working within an agile environment alongside senior engineers, you will bridge the gap between traditional enterprise backend development and cutting-edge AI technologies.

Responsibilities

• Design, develop, and maintain scalable backend services and RESTful endpoints using C# and ASP.NET Web API
• Implement efficient data access layers and handle database operations using Entity Framework Core and relational databases via SQL
• Utilize generative AI tools daily to accelerate coding, debugging, documentation, and unit-testing workflows
• Participate actively in agile ceremonies such as sprint planning, daily stand-ups, code reviews, and retrospectives to deliver iterative software increments
• Support the development of full-stack features using front-end technologies and cloud services
• Contribute to experiments involving AI agents and AI-native engineering principles as part of the product roadmap

Requirements

• 1+ years of experience with C#, object-oriented programming, and modern .NET ecosystem
• Proficiency in leveraging generative AI tools such as GitHub Copilot, ChatGPT, or Claude to enhance coding productivity, refactoring, and problem-solving
• Skills in SQL, including writing queries, designing relational schemas, and interacting with databases
• Experience building and consuming RESTful web services and APIs using ASP.NET Web API
• Familiarity with ORM concepts, database migrations, and LINQ queries using Entity Framework Core
• Understanding of Agile methodologies, Git workflows, and collaborative software development cycles
• Eagerness to learn, adapt quickly to new technologies, and take constructive feedback
• Analytical mindset with strong problem-solving skills and attention to detail when debugging or optimizing code
• Good written and verbal communication skills for effective collaboration within cross-functional agile teams
• Proficiency in English at a B2+ level

Nice to have

• Experience with MVC, Blazor, or broader web rendering technologies in .NET
• Basic or intermediate understanding of modern JavaScript/TypeScript front-end frameworks such as Angular or ReactJS
• Familiarity with cloud hosting, serverless functions, or core cloud infrastructure on Microsoft Azure or Amazon Web Services
• Exposure to building autonomous agents, multi-agent workflows, or utilizing orchestration libraries such as Semantic Kernel, AutoGen, or LangChain
• Interest or project experience in designing architectures built natively around LLMs and intelligent data flows rather than traditional post-hoc integrations