Join our team as a Custom Software Engineer and play a pivotal role in developing innovative enterprise-grade solutions. This full-time position focuses on leveraging modern frameworks and agile methodologies to design, code, and enhance components across various systems.

You will be instrumental in building scalable, high-performing applications tailored to meet specific business needs. We are seeking a professional with an AI-first mindset to drive innovation and efficiency through Generative and Agentic AI.

This role emphasizes hands-on development and practical solution design, with a focus on multi-cloud environments. You'll contribute to building secure, scalable enterprise applications and collaborate with cross-functional teams.

Design and implement custom software solutions, coding and enhancing components across applications.

Lead the design and delivery of AI-driven solutions using Generative and Agentic AI to tackle complex business challenges.

Develop, test, and deploy application-heavy solutions, writing clean code in languages like .NET Core (C#, ASP.NET) and Python.

Collaborate on designing scalable, cloud-native application architectures with a focus on performance and integration.

Build and maintain integrations using APIs, microservices, and event-driven patterns across multi-cloud platforms (AWS, Azure, Google Cloud).

Implement practical AI features and integrate them into enterprise workflows.

Work closely with DevOps teams on containerized solutions and CI/CD pipelines.

Optimize applications for performance and security through code reviews and adherence to best practices.

A Bachelor's degree in Computer Science, Engineering, or a related field is required.

We seek candidates with 5 to 12 years of hands-on experience in full-stack development, including at least 2 years in a lead developer or junior architect role.

Proficiency in .NET Core and Python for backend development is essential, along with hands-on experience in front-end frameworks like React or Angular.

Demonstrated experience building and deploying solutions in multi-cloud environments (AWS, Azure, Google Cloud) is crucial.

Basic exposure to AI/ML integration and practical experience with integration patterns and microservices architecture are necessary.

Familiarity with infrastructure tools like Docker, Kubernetes, and IaC (e.g., Terraform, BiCep) is required.

Working knowledge of DevOps practices, including CI/CD pipelines, is expected.

Strong debugging and problem-solving skills, clear communication, and an eagerness to learn are vital.