Technical Architect - AWS & Machine Learning

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.

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.

Designs and implements AWS cloud-native applications, distributed systems, and machine learning infrastructure. Leads architecture for SageMaker model deployment, inference, endpoints, and AI integrations while guiding engineering teams. Builds scalable event-driven services using AWS Lambda, SNS/SQS, EventBridge, DynamoDB, and Bedrock. Develops Python automation and ML integrations, applies Terraform and CI/CD, and ensures production ML workloads are secure, reliable, scalable, and cost-effective. Preferred experience includes NLP, embeddings, generative AI, OCR, MLOps, RAG, and vector databases. The summary above was generated by AI

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