Principal Solutions Architect

You have around 10 years of experience in AI/ML, Generative AI, and automation domains. As a highly skilled and visionary Solution Architect, your role will involve leading the design and implementation of cutting-edge solutions in Artificial Intelligence (AI), Generative AI, and Automation. You will need a combination of technical expertise, strategic thinking, and leadership to drive innovation and deliver scalable, high-impact solutions across the organization. Your key responsibilities will include designing end-to-end AI and automation solutions that are aligned with business goals and technical requirements. You will participate in business requirements discussions to arrive at solutions and technical architecture that consider all aspects of the business problem. It will be your responsibility to define architecture blueprints, integration patterns, and data pipelines for AI-driven systems. Additionally, you will evaluate and select appropriate AI models, automation tools (e.g., RPA, BPM), and cloud platforms to ensure scalability, security, and performance of deployed solutions. Staying current with emerging technologies and industry trends in AI, ML, NLP, computer vision, and automation will also be crucial. Providing technical leadership and mentorship to engineering teams, as well as participating in pre-sales and client engagements to define solution strategies and roadmaps, will be part of your role. The required qualifications for this position include a Bachelor's or Master's degree in computer science, Engineering, or a related field. You should have proven experience in architecting and deploying Generative AI solutions in production environments in Azure, GCP, AWS, etc. Strong knowledge of AI/ML frameworks such as TensorFlow, PyTorch, Scikit-learn, experience with automation platforms like UiPath, Automation Anywhere, Power Automate, proficiency in cloud platforms (AWS, Azure, or GCP) and containerization (Docker, Kubernetes), solid understanding of data engineering, APIs, and microservices architecture, as well as excellent communication and stakeholder management skills. Preferred qualifications for this position include certifications in cloud architecture or AI/ML (e.g., AWS Certified Machine Learning, Azure AI Engineer), experience with MLOps, model monitoring, and CI/CD pipelines, as well as familiarity with ethical AI practices and data privacy regulations.,