AI Solution Architect with AWS

JD:-

Experienced Senior AI Solution Architect to lead the design, architecture, and implementation of enterprise-scale Artificial Intelligence, Generative AI, and Agentic AI solutions on AWS. This role focuses on defining AI strategy, architecting scalable cloud-native solutions, establishing AI governance frameworks, and guiding engineering teams in delivering production-grade AI platforms and intelligent automation systems.

The position sits at the intersection of Large Language Models (LLMs), Generative AI, Agentic AI, Enterprise Architecture, Cloud-Native Engineering, Security, and Business Transformation, delivering scalable AI solutions using AWS AI/ML and cloud services.

Experience & Qualifications

• Bachelor’s degree in computer science, Engineering, Technology, or a related field, with 12–15 years of overall IT experience and at least 5+ years of hands-on experience architecting AI/ML, Generative AI, and cloud-native solutions on AWS.
• Strong expertise in core AWS services including Amazon Bedrock, SageMaker, Lambda, Step Functions, EventBridge, API Gateway, ECS/EKS, S3, DynamoDB, Aurora, OpenSearch, CloudWatch, and IAM.
• Advanced proficiency in Python and cloud-native application development for scalable AI and automation solutions.
• Proven experience defining enterprise AI architectures, reference frameworks, and strategic technology roadmaps.
• Hands-on experience designing Retrieval Augmented Generation (RAG) solutions, vector search architectures, model fine-tuning, and knowledge management platforms.
• Demonstrated expertise in implementing Large Language Model (LLM) solutions, foundation models, AI agents, multi-agent systems, and intelligent workflow automation.
• Strong understanding of enterprise integration patterns, microservices architectures, APIs, event-driven systems, and hybrid cloud environments.
• Experience leading architecture reviews, technology selection, design governance, solution assessments, and technical decision-making processes.
• Proven ability to drive performance optimization, scalability, reliability, resiliency, security, and cost optimization across AI workloads.
• Healthcare domain experience is desirable, along with knowledge of AI safety, governance, compliance, privacy regulations, and responsible AI practices.
• AWS certifications such as AWS Solutions Architect Professional, AWS Machine Learning Specialty, or AWS AI certifications are preferred. Experience with LangChain, LangGraph, LlamaIndex, Semantic Kernel, and Agentic AI frameworks is a plus.
• Excellent communication, stakeholder management, leadership, mentoring, and executive presentation skills with the ability to engage both technical and non-technical audiences.

Roles & Responsibilities

• Design and lead enterprise-scale AI, Generative AI, and Agentic AI solution architectures on AWS using Amazon Bedrock, SageMaker, foundation models, and cloud-native services.
• Define AI architecture standards, reference frameworks, reusable design patterns, and technology roadmaps to accelerate enterprise-wide AI adoption and innovation.
• Architect intelligent AI agents, multi-agent systems, and autonomous workflows that support reasoning, orchestration, tool integration, workflow execution, and human-in-the-loop collaboration.
• Design and implement advanced RAG, vector search, and knowledge management solutions leveraging Amazon OpenSearch, DynamoDB, Aurora, S3, and vector databases.
• Establish scalable AI/ML platform architectures for model training, fine-tuning, evaluation, deployment, MLOps, lifecycle management, and operational excellence.
• Drive enterprise integration strategies by connecting AI solutions with business applications, APIs, data platforms, microservices, event-driven architectures, and hybrid cloud environments.
• Ensure AI solutions meet enterprise standards for security, governance, compliance, privacy, responsible AI, reliability, performance, observability, resiliency, and cost optimization.
• Develop AI governance frameworks and guardrails to address risks such as hallucinations, prompt injection, model bias, adversarial attacks, data privacy, and AI misuse.
• Lead architecture reviews, design workshops, proof-of-concepts, technical evaluations, production support, and strategic technology decision-making with business and technology stakeholders.
• Mentor and guide cross-functional teams, including AI engineers, architects, data scientists, DevOps, security, and business stakeholders, to deliver scalable, production-ready AI solutions and drive continuous improvement.