Job Summary
We are seeking an experienced AI Architect to design, build, and deploy enterprise-scale AI and LLM solutions. The role requires a hands-on technical leader with expertise in AI engineering, cloud infrastructure, software development, and advanced agentic workflows using AWS Agent Core. The ideal candidate will architect end-to-end AI/ML platforms, develop production-grade applications, establish scalable AI delivery practices, and mentor engineering teams.
Key Responsibilities
• Design and implement end-to-end AI/ML architectures and production-ready solutions using modern AI frameworks and cloud-native technologies.
• Architect, provision, and manage secure, scalable, and resilient AI infrastructure on AWS using Terraform and Infrastructure-as-Code principles.
• Build, optimize, and deploy advanced multi-agent systems and orchestration frameworks using AWS Agent Core, Amazon Bedrock Agents, and Knowledge Bases.
• Develop and maintain data, model, and inference pipelines integrating LLMs, vector databases, and enterprise applications.
• Implement Retrieval-Augmented Generation (RAG) solutions for enterprise AI use cases.
• Develop and optimize prompt engineering strategies to improve AI solution performance and outcomes.
• Establish and drive best practices across AI Engineering, MLOps, and LLMOps.
• Ensure AI deployments are scalable, reliable, secure, and governed.
• Provide technical leadership and mentorship to engineering teams.
• Collaborate with business and technology stakeholders to define and deliver AI initiatives.
• Lead AI solutions from proof of concept through production implementation.
Required Qualifications
• Strong experience designing and deploying AI/ML and Generative AI solutions from proof of concept through production implementation.
• Strong experience with AI engineering, cloud infrastructure, and software development.
• Hands-on experience architecting and implementing enterprise-scale AI and LLM solutions.
• Strong experience with AWS cloud technologies and cloud-native AI architectures.
• Experience with Terraform and Infrastructure-as-Code principles.
• Experience building multi-agent AI systems and orchestration frameworks.
• Experience with AWS Agent Core, Amazon Bedrock Agents, and Knowledge Bases.
• Experience developing data, model, and inference pipelines.
• Experience integrating LLMs, vector databases, and enterprise applications.
• Strong experience implementing Retrieval-Augmented Generation (RAG) solutions.
• Experience with prompt engineering and optimization strategies.
• Strong understanding of AI Engineering, MLOps, and LLMOps practices.
• Demonstrated technical leadership, mentoring, and stakeholder collaboration skills.
