Enterprise Software Architect (EMEA)

We are looking for a highly skilled .NET Enterprise Software Architect to spearhead the technical vision and design of our enterprise AI-driven translation platform. You will help bring together many internally developed tools into a suite of products that meet complex language localisation requirements with robust, scalable, and high-performance software.

In this role, you will lead technology teams, enforce architectural best practices, and integrate cutting-edge machine learning and Large Language Models (LLMs) into our core .NET ecosystem.

Responsibilities

• Architecture & Design: Design scalable, distributed .NET systems optimized for processing massive volumes of multilingual textual and audio data.

• AI & LLM Integration: Architect secure API integrations and middleware connecting .NET workflows with translation models, LLMs, and custom NLP pipelines.

• Analyzing the existing tools used in Translation Management services and building a suite of products or a platform that could be customized for individual customer needs with minimal configuration and fine-tuning.

• Technology Leadership: Define technical standards, coding guidelines, and architectural blueprints tailored for AI-assisted localization pipelines.

• Mentorship: Provide guidance and technical mentoring to developers, conducting thorough design and code reviews to ensure quality deliverables.

Qualifications

Education, skills, and experience

• Bachelor’s degree in Computer Science, Software Engineering, or a related field.

• Minimum 10 to 12+ years in software development, with at least 3 years in a dedicated .NET architecture role.

• Hands-on System Design experience in building large, complex microservices-based solutions.

• Deep expertise in C#, .NET 8+, ASP.NET Core, Java, ReactJS and asynchronous programming patterns.

• Proven experience integrating AI frameworks, vector databases, or cognitive services (e.g., Azure OpenAI, Semantic Kernel) into .NET applications.

• Strong proponent of Agile process and industry-standard SDLC methodologies.

• Strong knowledge of cloud-native infrastructure (Azure/AWS), message brokers (RabbitMQ/Kafka), and managing large-scale dataset storage.

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Originally posted on Himalayas