Senior Technical Architect AI & GenAI Solutions

Job Summary : As a Senior Technical Architect, you will be responsible for designing and implementing robust, scalable, and high-performance AI and Generative AI solutions. You will work closely with crossfunctional teams to ensure that our technical architecture aligns with business goals and industry best practices. Key Responsibilities: Design and develop AI and Generative AI architecture solutions that meet business requirements. Be hands-on in coding, prototyping, and testing solutions. Collaborate with stakeholders to understand their needs and translate them into technical specifications. Apply AI solutions across various industries and business functions Develop versatile AI models that can be adapted for multiple use cases • Lead the technical design and development of AI/Gen AI-powered projects. Integrate and enhance existing tools and systems to leverage AI/Gen AI capabilities Develop and implement APIs for seamless integration with legacy and SaaS applications • Ensure compliance with industry standards and best practices during integration Ensure the scalability, performance, and security of solutions. Provide technical guidance and mentorship to development teams. Conduct code reviews and ensure adherence to best practices and coding standards. Stay updated with the latest industry trends and technologies. Troubleshoot and resolve technical issues as they arise. Document technical designs and processes. Qualifications: Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Proven experience as a Technical Architect or similar role. Strong knowledge of AI, Machine Learning, and Generative AI technologies. Proficiency in one or more of following programming languages Java, C++, Python, R Experience with cloud platforms (e.g., AWS, Azure, Google Cloud). Excellent problem-solving and analytical skills. Strong communication and interpersonal skills. Ability to work effectively in a collaborative team environment. Strong domain understanding Additional Qualifications: Experience with microservices architecture. Knowledge of DevOps practices and tools. Certification in relevant technologies or frameworks. Experience with Natural Language Processing (NLP) and Computer Vision.