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# Technical Architect - AWS & Machine Learning

- **Company:** Perficient
- **Location:** Denver, CO, United States (Remote available)
- **Experience:** Expert
- **Salary:** $82,000.0 - $160,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Application Services, Cloud Engineering, Encodings, Continuous Integration, Distributed Systems, Amazon DynamoDB, Github, Python (Programming Language), Machine Learning, Search Technologies, Software Deployment, Software Engineering, Enterprise Software Applications, Large Language Models, AWS Lambda, Event Driven Architecture, Machine Learning Operations, Amazon Simple Queue Service (SQS), Terraform, Serverless Computing
- **Published:** September 17, 2026
- **Apply:** https://www.builtincolorado.com/job/technical-architect-aws-machine-learning/11260256?handler=ApplyRedirect

## About the Role

The ideal candidate combines deep AWS expertise, software engineering experience, and significant hands-on experience with Amazon SageMaker and production machine learning deployments. Experience supporting NLP, embeddings, and AI-powered applications is highly desirable., * 5+ years of experience in software engineering, cloud engineering, technical architecture, or a related discipline.

 * Strong experience designing and implementing AWS-based solutions in production environments.
 * Hands-on experience with Amazon SageMaker, including model deployment, endpoint management, inference workflows, and integration with enterprise applications.
 * Experience supporting production machine learning workloads within AWS environments.
 * Hands-on expertise with AWS services including:
 + Lambda
 + SNS/SQS
 + EventBridge
 + DynamoDB
 + SageMaker
 * Experience building and supporting highly scalable distributed systems.
 * Strong understanding of asynchronous processing and event-driven architectures.
 * Hands-on experience with:
 + Terraform
 + Python
 + GitHub Actions
 + CI/CD automation
 * Ability to balance architectural design responsibilities with hands-on technical implementation., * Experience deploying and managing NLP, embedding, and generative AI solutions using Amazon SageMaker.
 * Understanding of embedding models, vectorization techniques, semantic search, and retrieval-based AI architectures.
 * Experience implementing OCR and document-processing workflows utilizing machine learning services.
 * Experience integrating SageMaker-hosted models into production applications and distributed architectures.
 * Familiarity with MLOps concepts, model lifecycle management, monitoring, and AI application deployment best practices.
 * Experience implementing AI/ML solutions using Amazon Bedrock or similar generative AI platforms.
 * Familiarity with Retrieval-Augmented Generation (RAG) architectures and vector databases.

## Description

We are seeking a hands-on Technical Architect to lead the design and implementation of cloud-native applications, distributed systems, and machine learning infrastructure within AWS. This individual will play a key role in defining technical architecture, guiding development teams, and delivering scalable solutions that support AI/ML-driven business capabilities., * Design and implement scalable, secure, and highly available solutions within AWS.

 * Architect machine learning-enabled applications leveraging Amazon SageMaker for model deployment, inference, endpoint management, and integration with downstream services.
 * Define architecture patterns and best practices for distributed, event-driven applications.
 * Lead the technical design and implementation of cloud-native services utilizing:
 + AWS Lambda
 + SNS/SQS
 + EventBridge
 + DynamoDB
 + Amazon SageMaker Endpoints
 + Amazon Bedrock
 * Collaborate closely with software engineers, data scientists, and product teams to operationalize machine learning solutions and AI-driven workflows.
 * Contribute hands-on development using Python for application services, automation, machine learning integrations, and SageMaker-based processing solutions.
 * Ensure machine learning workloads are scalable, reliable, secure, and cost-effective.