Gen AI Architect

About this opportunity:

We're seeking an experienced GenAI/AI Architect to lead the design and implementation of enterprise-scale artificial intelligence solutions. You'll own the complete architecture journey—from strategy through production deployment—working across technical and business teams to translate AI capabilities into measurable business value. This role is ideal for someone who combines deep technical expertise with the ability to navigate complex organizational landscapes and emerging technology trends.

What you will do:

• Design end-to-end architectures for production-grade GenAI/AI systems, establishing reference architectures and reusable patterns for enterprise adoption
• Evaluate and recommend large language models, vector databases, and orchestration frameworks based on performance, cost, compliance, and security requirements
• Architect retrieval-augmented generation (RAG) pipelines, agentic workflows, and multi-agent systems for scalable deployment
• Collaborate with data scientists, security teams, developers, and business leaders to align technical solutions with organizational needs
• Embed governance, privacy, and ethical frameworks into AI systems from design through deployment
• Guide teams on model lifecycle management, including deployment strategies, monitoring, performance optimization, and handling model drift
• Evaluate tools and frameworks for system integration and interoperability
• Drive adoption of cloud-native AI services and optimize for scalability and performance
• Stay informed on GenAI/AI advancements and regulations, translating insights into actionable strategic roadmaps

The skills you bring:

• Bachelor's or master's degree in computer science, statistics, engineering, or related field
• Proven track record architecting large-scale AI/ML systems in production environments
• Deep expertise in Python, machine learning frameworks (PyTorch, TensorFlow), and deep learning practices
• Strong knowledge of large language model architectures, fine-tuning techniques, and deployment strategies
• Hands-on experience with RAG pipelines, embeddings, and vector databases
• Familiarity with agentic GenAI frameworks and integration protocols