We are seeking a highly skilled and visionary .NET AI Lead/ Architect to lead the design, development, and integration of AI-powered solutions within our enterprise .NET applications. This role requires a deep understanding of .NET architecture and hands-on experience in integrating artificial intelligence and machine learning models into scalable, secure, and performant applications.

Key Responsibilities :

- Design/Architect end-to-end .NET solutions with integrated AI/ML components (e.g., predictive models, NLP, computer vision, recommendation engines).

- Collaborate with data scientists and ML engineers to integrate trained models (TensorFlow, PyTorch, ONNX, etc.) into .NET-based production environments (e.g., via APIs, containers, or embedded libraries).

- Define and drive AI integration strategies, including model serving, inferencing pipelines, and continuous learning mechanisms.

- Lead the development of microservices-based architectures with AI-driven services using .NET Core, C#, and Azure/AWS services.

- Ensure security, scalability, and performance of AI-enhanced solutions.

- Stay up to date with emerging trends in AI and .NET ecosystem and bring innovative ideas to the team.

- Mentor developers on best practices in AI integration and .NET architectural design.

- Collaborate with stakeholders to translate business requirements into technical designs involving intelligent automation.

Required Skills & Qualifications :

- Bachelors or Masters degree in Computer Science, Engineering, or related field.

- 10+ years of experience in software architecture and development using .NET/.NET Core (C#).

- 3+ years of hands-on experience integrating AI/ML solutions into enterprise applications.

- Strong understanding of ML lifecycle, model deployment (e.g., REST APIs, ONNX Runtime, Azure ML, ML.NET), and inferencing in .NET applications.

- Good working experience in front end technologies like Angular

- Experience with cloud platforms (Azure preferred; AWS or GCP acceptable), especially AI-related services (Azure Cognitive Services, AWS SageMaker, etc.).

- Proficiency in containerization and orchestration technologies like Docker and Kubernetes.

- Experience in DevOps and CI/CD pipelines for AI/ML deployment.

- Familiarity with ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data handling in enterprise environments.

- Strong understanding of software architecture patterns: microservices, event-driven, domain-driven design (DDD), etc.

- Excellent problem-solving, communication, and leadership skills.