Budget: 30-40L (leads/architects, based on role and experience)
Experience- 10+yrs
Candidates should be good to communicate and presentable.
Stable profiles/ no job hoppers.
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:
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:
Bachelor's or Master's 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,
), 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.