Associate AWS Solutions Architect- AI/ML in United

This is a fully remote position with occasional travel as necessary for conferences or customer meetings., As an Associate Solutions Architect - AI/ML in Cloudelligent’s Data & AI Practice, you will design and deliver AI-enabled solutions on AWS for customers, focusing on Amazon Bedrock, Amazon SageMaker, AWS Lambda, Generative AI, and Agentic AI., You will work with senior solutions architects, service delivery managers, and engineers across the full engagement: technical discovery, solution design support, hands-on implementation, and customer handover. You will deliver working AI solutions such as RAG-based assistants, agentic workflows, and AI-assisted customer service, and you will modernize customer applications by embedding AI capabilities into them., This is a hands-on, customer-facing role with technical leadership responsibilities for assigned projects or workstreams., * Claude certifications - Associate, Developer, or Architect (Foundations), or Architect (Professional) \n

• Experience integrating Amazon Connect with Amazon Lex and Amazon Bedrock for conversational self-service and AI-assisted customer support. \n

• Experience implementing agent workflows, tool integrations, or human-approval steps within AI applications. \n

• Familiarity with CI/CD, automated testing, Terraform or AWS CloudFormation, and monitoring of production AI workloads. \n

Applicants must be authorized to work in the United States. This position does not offer employment visa sponsorship., * 3+ years of experience in cloud engineering, software development, data engineering, or a related technical role, including hands-on experience building and deploying solutions on AWS. \n

• Practical experience implementing AI/ML or Generative AI solutions through customer projects, internal solutions, or substantial proofs of concept. \n

• Hands-on experience with Amazon Bedrock and/or Amazon SageMaker, including model APIs and AI application integration. \n

• Hands-on experience building event-driven solutions on AWS (Lambda, Fargate, Cognito, IAM, etc.) and integrating AI capabilities into them. \n

• Understanding of RAG, embeddings, vector search, prompt design, and methods for evaluating AI outputs. \n

• Proficiency in Python and SQL, with experience integrating REST APIs, working with Git, and building data pipelines for AI/ML workloads using services such as AWS Glue, Amazon Athena, Amazon Redshift, or Amazon Kinesis. \n

• Strong troubleshooting, documentation, and communication skills, including the ability to break designs into actionable tasks, present progress to technical and non-technical audiences, and work effectively with customers and distributed teams. \n

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• Run technical discovery sessions with customers to understand business problems, data readiness, and use cases, and translate them into AI solution designs and implementation plans. \n

• Build Generative AI and Agentic AI solutions on AWS, including RAG, AI assistants, and agent workflows that connect models to business systems, APIs, and tools, using Amazon Bedrock (foundation models, Knowledge Bases, Agents, Guardrails) and Amazon SageMaker (training, fine-tuning, deployment, and endpoints). \n

• Build AWS Lambda-based integration and orchestration layers that connect AI models to customer applications, data sources, and third-party systems. \n

• Support application modernization through AI by embedding capabilities such as intelligent search, summarization, document processing, and conversational interfaces into existing customer applications. \n

• Implement Amazon Connect contact center solutions enhanced with AI, integrating Amazon Lex, Amazon Bedrock, and AWS Lambda for conversational self-service and AI-assisted agents. \n

• Develop data ingestion, embedding, and retrieval pipelines that feed AI/ML workloads, using services such as Amazon S3, AWS Glue, Amazon Athena, and vector stores. \n

• Test and evaluate AI solutions for output quality, accuracy, latency, reliability, and cost, iterating on prompts, retrieval strategies, and model selection, and take proof of concept to secure, supportable production environments. \n

• Lead day-to-day technical implementation for assigned projects or workstreams, keeping delivery aligned with approved architecture and customer requirements, and act as the customer’s technical point of contact through workshops, demonstrations, and status updates. \n

• Support presales through AI proof of concept, technical validation, and implementation estimates, and produce documentation, deployment guides, and runbooks for customer handover. \n

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