Join our team as a Custom Software Engineer and play a pivotal role in crafting bespoke software solutions. You'll be instrumental in designing, coding, and enhancing components across diverse systems and applications. Utilizing modern frameworks and agile methodologies, we aim to deliver scalable, high-performance solutions precisely tailored to meet specific business requirements, driving innovation and efficiency.

Lead the design and implementation of AI-driven solutions by leveraging Generative and Agentic AI to tackle intricate business challenges. Automate processes, and embed intelligent insights into enterprise workflows for tangible business impact.

Your hands-on coding will involve developing, testing, and deploying robust application solutions, writing clean code in languages such as Java Spring Boot and Python for backend systems, and contributing to front-end development with React or Angular.

Collaborate on designing scalable, cloud-native application architectures with a focus on performance, maintainability, and multi-cloud integration. Build and maintain integrations using APIs, microservices, and event-driven patterns, and deploy applications across AWS, Azure, and Google Cloud.

Implement practical AI features, support DevOps teams with containerized solutions and CI/CD pipelines, and optimize applications for performance and security. Work closely with cross-functional teams to translate requirements into technical solutions, and stay updated on the latest full-stack development trends, cloud technologies, and AI advancements.

We are seeking a dynamic professional with 5 to 12 years of hands-on full-stack development experience, including at least 2 years in a lead developer or junior architect capacity focusing on application-heavy systems. Demonstrated experience building and deploying solutions in multi-cloud environments (AWS, Azure, Google Cloud) is essential.

Possess strong proficiency in Java Spring Boot and Python for backend development, coupled with hands-on experience in front-end frameworks like React or Angular. Basic exposure to AI/ML integration, practical experience with integration patterns and microservices architecture, and familiarity with infrastructure tools like Docker, Kubernetes, and IaC are required.

A working knowledge of DevOps practices, including CI/CD pipelines and monitoring tools, along with an understanding of secure coding practices and basic enterprise security standards, is necessary. Strong debugging and problem-solving skills with a hands-on, collaborative mindset are key. Clear communication abilities to engage with both technical and non-technical stakeholders are valued. An eagerness to learn and adapt to new technologies is also crucial.