We are seeking an experienced AWS Solution Architect with deep expertise in designing, deploying, and operating production-scale Agentic AI platforms. This role requires hands-on experience building autonomous and multi-agent systems that leverage enterprise knowledge, long-term memory, reasoning workflows, and orchestration frameworks to solve complex business problems. The ideal candidate has successfully moved AI solutions from proof-of-concept to production, demonstrating measurable business outcomes, operational excellence, scalability, governance, security, and observability. This individual will architect and implement cloud-native AI platforms on AWS, integrating large language models, knowledge graphs, vector stores, memory frameworks, and enterprise data ecosystems to enable intelligent automation and decision-making. The architect will work closely with engineering, data, security, and business teams to establish enterprise patterns for agent orchestration, knowledge management, memory persistence, evaluation frameworks, prompt governance, and production operations. ESSENTIAL DUTIES AND RESPONSIBILITIES Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time. 1. Implements software architecture and engineering approaches for complex initiatives within the job area, contributing to technical plans and working to achieve operational targets with major impact on results. 2. Adopts and refines advanced software engineering standards, practices, and governance mechanisms for the job area, influencing how multiple teams improve quality, reliability, and delivery. 3. Collaborates with senior engineers, product partners, and architecture teammates to shape technology approaches for the domain, providing deep technical insight and proposing solution patterns that inform local roadmaps and priorities. 4. Leads the end-to-end technical design and implementation of scalable, secure, and highly available software solutions for the job area, producing patterns and examples that other technical professionals can follow. 5. Independently troubleshoots and resolves complex technical issues in the area of responsibility, designing innovative architectures and performance, reliability, and scalability improvements that advance business objectives. 6. Provides ongoing technical guidance, coaching, and training to other engineers, delegating and reviewing work from lower-level technical professionals and raising the technical bar through design reviews and knowledge sharing. 7. Evaluates emerging technologies and techniques relevant to the job area, building prototypes and solution concepts that contribute measurable input into new features, products, or capabilities. 8. Contributes to the development of long-term technical goals and plans for the area of responsibility through well-reasoned recommendations, design proposals, and implementation experience. 9. Leads large or complex initiatives within the job area, coordinating and delegating technical work that may span outside the immediate team, and ensuring cohesive, high-quality outcomes with limited supervision. Qualifications Required Qualifications The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions. 1. Bachelor's degree in Computer Science, Software Engineering, or related field. 2. Minimum of 7 years of professional experience in software development. 3. Deep knowledge of multiple programming languages, software architecture, and design principles. 4. Deep understanding of software development lifecycle, testing, deployment, and security practices. Strongly Preferred Key Skills and Qualifications Key Responsibilities • Design and architect production-grade Agentic AI solutions on AWS. • Build autonomous and multi-agent systems capable of planning, reasoning, tool usage, and workflow execution. • Design enterprise knowledge architectures utilizing: • Knowledge Graphs • Vector Databases • Semantic Retrieval • Retrieval-Augmented Generation (RAG) • Hybrid Search Frameworks • Implement memory architectures, including: • Short-term conversational memory • Episodic memory • Semantic memory • Long-term agent memory • Context persistence frameworks • Design agent orchestration patterns using AWS-native and open-source technologies. • Establish production deployment patterns, monitoring, guardrails, and governance controls. • Lead architecture reviews and technical design sessions across engineering organizations. • Implement evaluation frameworks for agents, prompts, workflows, and model performance. • Partner with Security and Risk teams to implement: • Prompt Injection Detection • Data Leakage Prevention • AI Governance Controls • Human-in-the-Loop Approvals • Define scalable reference architectures and best practices for future AI initiatives. Required Qualifications • 10+ years of software engineering, cloud architecture, or distributed systems experience. • 5+ years designing solutions on AWS. • 3+ years implementing Generative AI and LLM-based systems. • Proven experience deploying Agentic AI solutions into production environments serving real users and business processes. • Deep experience with: • Amazon Bedrock • SageMaker • EKS • Lambda • Step Functions • API Gateway • OpenSearch • Aurora • Neptune • Strong knowledge of: • Agent Frameworks (LangGraph, CrewAI, AutoGen, Semantic Kernel, OpenAI SDK Agents, etc.) • MCP (Model Context Protocol) • Tool Calling Architectures • Agent Orchestration • Hands-on experience implementing: • Vector Databases • Embedding Pipelines • RAG Systems • Knowledge Graph Solutions • Hybrid Retrieval Architectures • Experience creating CI/CD pipelines for AI workloads. • Expertise in cloud security, identity, governance, and operational excellence. Preferred Qualifications • Experience building enterprise AI platforms rather than isolated use cases. • Hands-on implementation of memory systems for autonomous agents. • Experience with Knowledge Graph technologies, including Amazon Neptune. • Experience implementing AI evaluation frameworks and model observability. • Experience designing AI governance, prompt registries, model registries, and approval workflows. • AWS Professional Architect certification. • Experience within financial services or highly regulated industries.