Technical Architect - AWS & Machine Learning
About The Position
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. 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.
Requirements
- 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.
Nice To Haves
- 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.
Responsibilities
- 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.
Benefits
- Information regarding the benefits available for this position are in our benefits overview [https://perficient.sharepoint.com/:b:/s/TADocs/IQDIWAqZ5bEQS6RUrBBOTvqFAY24gqK0FLYuXFCEeq17-7M].
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What This Job Offers
Job Type
Full-time
Career Level
Senior
Education Level
No Education Listed
