GenAI & Agentic AI Architect

We are seeking a highly experienced GenAI & Agentic AI Architect with strong hands-on expertise to lead the design, development, and implementation of advanced AI solutions for our clients. This role requires a seasoned technical leader with over 8 years of architecture experience and deep knowledge of Azure and/or AWS cloud ecosystems, combined with strong capabilities in containerization, orchestration, observability, and modern AI frameworks., * Architect and develop scalable, high-performance Generative AI solutions on Azure and/or AWS platforms

• Design, build, and manage containerized AI applications using Docker and orchestrate deployments via Kubernetes
• Establish and maintain observability frameworks for LLMs, including performance monitoring, system health, and usage analytics
• Develop and implement machine learning models using Python or R, leveraging frameworks such as TensorFlow, PyTorch, or scikit-learn
• Build and deploy AI/ML solutions using cloud-native services such as AWS SageMaker, Azure AI, or Google Vertex AI
• Design and implement multi-agent systems and Retrieval-Augmented Generation (RAG) architectures
• Define and enforce standards for prompt engineering, vector search, and model orchestration
• Build and manage enterprise-grade MLOps pipelines covering model training, fine-tuning, evaluation, deployment, and monitoring
• Collaborate with cross-functional teams to integrate AI capabilities into enterprise products and platforms
• Lead system optimization, performance tuning, and troubleshooting of AI-driven applications
• Establish best practices for secure, scalable, and maintainable AI architectures
• Mentor and guide AI engineering teams on emerging technologies and implementation best practices
• Stay current with advancements in Generative AI, Agentic AI, and cloud technologies

• Minimum 8+ years of experience in a technical architecture role
• Proven expertise in Azure and/or AWS cloud architectures
• Strong hands-on experience with Docker and Kubernetes
• Proficiency in Python or R with experience in TensorFlow, PyTorch, or scikit-learn
• Deep knowledge of MLOps practices and model deployment on AWS SageMaker, Azure AI, or Google Vertex AI
• Experience with multi-agent orchestration and RAG architectures
• Solid understanding of prompt engineering, vector search, and LLM observability frameworks
• Experience with Spring Boot and microservices architecture
• Strong understanding of enterprise AI security, scalability, and governance principles