Gen AI & Agentic AI Architect

Key Responsibilities

• Design and implement scalable, high-performance Generative AI solutions on Azure and/or AWS platforms
• Plan, develop, and oversee containerized AI applications using Docker, and coordinate deployments through Kubernetes
• Create and sustain observability capabilities for LLMs, covering performance monitoring, system health, and usage analytics
• Develop and deploy machine learning models using Python or R, utilizing frameworks such as TensorFlow, PyTorch, or scikit-learn
• Build and release AI/ML solutions with cloud-native services including AWS SageMaker, Azure AI, or Google Vertex AI
• Architect and implement multi-agent systems and Retrieval-Augmented Generation (RAG) frameworks
• Define and enforce standards for prompt engineering, vector search, and model orchestration
• Develop and manage enterprise-level MLOps pipelines spanning model training, fine-tuning, evaluation, deployment, and monitoring
• Partner with cross-functional teams to embed AI capabilities into enterprise products and platforms
• Drive application optimization through performance tuning and troubleshooting for AI-driven systems
• Set up best practices for secure, scalable, and maintainable AI architectures
• Mentor and support AI engineering teams with emerging technologies and implementation best practices
• Monitor and apply the latest advancements in Generative AI, Agentic AI, and cloud technologies

Required Skills & Qualifications

• Minimum 8+ years of experience in a technical architecture role
• Demonstrated expertise in Azure and/or AWS cloud architectures
• Strong practical experience with Docker and Kubernetes
• Proficiency in Python or R, including experience with TensorFlow, PyTorch, or scikit-learn
• In-depth knowledge of MLOps practices and experience deploying models on AWS SageMaker, Azure AI, or Google Vertex AI
• Experience with multi-agent orchestration and RAG architectures
• Comprehensive 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