.NET Gen AI Developer

Smart IMS•Austin, TX

12h•Onsite

About The Position

As a .NET Gen AI Developer, you will contribute to the design, development, and modernization of enterprise applications by combining .NET engineering expertise with Generative AI-assisted development practices. This role focuses on building scalable, cloud-native, event-driven solutions while leveraging AI-powered engineering tools to accelerate software delivery, improve code quality, and support intelligent development workflows across the entire software development lifecycle. You will work closely with architects, product owners, developers, and cross-functional teams to deliver reliable, resilient, and high-performance business solutions. The ideal candidate will possess strong experience in distributed systems, cloud platforms, event streaming technologies, and AI-assisted software engineering practices within complex enterprise environments.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, Information Technology, or a related technical field, or equivalent professional experience.
5+ years of professional software development experience building scalable .NET applications, web services, or cloud-based solutions.
Strong hands-on experience with .NET development, distributed systems, event-driven architectures, and Kafka.
Experience designing and implementing high-performance, resilient applications in highly available environments.
Working knowledge of IBM MQ, Kafka, Splunk, JSON, XML, caching technologies, performance profiling, and observability practices.
4+ years of experience developing and deploying cloud-native applications using GCP, Docker, Kubernetes, microservices, and CI/CD pipelines.
Experience working with cloud platforms such as GCP, AWS, PCF, or similar environments.
2+ years of experience with relational and NoSQL databases such as MongoDB, Aerospike, PostgreSQL, or equivalent technologies.
Experience supporting enterprise modernization and digital transformation initiatives.
Minimum 1 year of hands-on experience with AI/ML implementations, production AI solutions, or AI-assisted engineering workflows.
Practical experience with Large Language Models (LLMs) such as GPT, Claude, Gemini, PaLM, or similar enterprise AI platforms.
Demonstrated experience using AI coding assistants such as GitHub Copilot, Claude Code, or equivalent tools throughout the SDLC.
Knowledge of agentic workflows, prompt engineering, custom instructions, and AI-assisted software delivery methodologies.
Experience with Agile development methodologies and working within Agile environments.
Familiarity with Atlassian tools including Jira and Confluence.
Strong understanding of software architecture, microservices, cloud-native design, and distributed computing principles.
Experience building and consuming REST APIs and integrating enterprise applications.
Strong analytical thinking, troubleshooting, and problem-solving skills.
Excellent verbal and written communication skills with the ability to collaborate effectively across cross-functional teams.
Experience within financial services or other highly regulated environments is preferred.

Nice To Haves

Master’s degree in Computer Science, Artificial Intelligence, Information Technology, Software Engineering, or a related field.
Experience with Test-Driven Development (TDD), Behavior-Driven Development (BDD), QA automation, and quality engineering practices.
Experience creating architecture diagrams, technical specifications, runbooks, operational documentation, and implementation guides.
Knowledge of performance engineering, reliability testing, resiliency validation, and operational readiness best practices.
Exposure to enterprise AI implementation programs and AI-assisted SDLC transformation initiatives.
Experience mentoring team members on .NET development, distributed systems, cloud-native architectures, and responsible AI-assisted engineering practices.

Responsibilities

Design and implement scalable, resilient, and event-driven applications using .NET, Kafka, and cloud-native architecture patterns.
Develop, modernize, and support high-volume services, APIs, and distributed application components.
Collaborate with architects, product owners, and technical teams to translate business requirements into technical solutions.
Leverage Generative AI coding assistants to accelerate development, refactoring, testing, documentation, automation, and troubleshooting activities.
Utilize prompt engineering, custom instructions, agentic workflows, and spec-driven development practices to improve engineering productivity.
Implement automated testing, code reviews, performance analysis, observability, and resiliency best practices.
Support modernization efforts by transforming legacy systems into distributed services and cloud-native applications.
Contribute to the adoption of responsible AI-assisted development practices across engineering teams.
Participate in Agile development activities, sprint planning, and continuous delivery processes.
Develop and maintain technical documentation, architecture artifacts, and implementation guidelines.