AI Architect

AI ArchitectBasic Responsibilities ( Must-Haves): 9+ years of experience in Designing and architecting solutions with artifacts and technical documents using LLM, Generative AI, RAG, and Agentic AI applications. Strong understanding of LLM, Generative AI, RAG, and Agentic AI applications. Collaborating with cross-functional teams to understand business requirements and translate them into technical specifications. Creating detailed technical designs and documentation that accurately reflect the proposed solution and its components. Developing and implementing various types of solutions using programming languages such as Python, .Net , Java etc. Working with data scientists, engineers, and other stakeholders to understand and implement machine learning algorithms such as regression, classification, and clustering. Leading teams on the latest AI tools and solutions, mentoring junior architects and developers, and providing guidance on best practices. Excellent problem-solving skills and ability to think critically about complex systems. Experience working with cloud platforms such as Azure Cloud or AWS Cloud. Strong leadership, communication, and teamwork skills. Ability to learn quickly and adapt to new technologies. Excellent problem solving skills Preferred Responsibilities (Nice-to-Haves): Participate in the design and implementation of AI-powered solutions for clients Develop and maintain technical documentation, including architecture diagrams and solution designs Provide training and support to internal stakeholders on AI technologies and solutions Strong communication and collaboration skills, with the ability to work effectively in a team environment. Any Other: Possessing a certificate in ML (Machine Learning) and an Architect Certificate in Azure Cloud or AWS Cloud. Experience working with large datasets and data processing tools, such as Spark or Hadoop. Familiarity with Agile development methodologies and version control systems, such as Git.Key Responsibilities: Participate in the development of proof-of-concepts (POCs) and pilot projects to demonstrate AI-powered solutions Collaborate with data science teams to design and implement machine learning models and algorithms Collaborate with sales teams to identify new business opportunities and develop go-to-market strategies