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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI/ML Engineer, Amazon Global Data Center Ops Central Insight and Analytics Team - **Company:** Amazon.com, Inc. - **Location:** Seattle, WA, United States - **Experience:** Experienced - **Salary:** $143,700.0 - $194,400.0 - **Contract:** Internship / Graduate position - **Skills:** Training Data, A/B Testing, Artificial Intelligence, Data Analysis, Code Review, Continuous Integration, Data Centers, Data Transformation, Python (Programming Language), Machine Learning, Operational Data Store, Tensorflow, Azure Machine Learning, Software Engineering, Feature Engineering, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, Cloudformation, Information Technology, Build Process, Machine Learning Operations, Software Coding, Terraform, Software Version Control, Data Pipelines - **Published:** August 25, 2026 - **Apply:** https://www.amazon.jobs/en/jobs/10513234/ai-ml-engineer-amazon-global-data-center-ops-central-insight-and-analytics-team ## About the Role 3+ years of non-internship professional software development experience - Bachelor's degree in Computer Science, Machine Learning, or related field (or equivalent experience) - 2+ years deploying ML models to production environments - Strong Python proficiency + experience with ML frameworks - Experience with LLM APIs and prompt engineering - Experience with cloud ML services - Experience building data pipelines for ML (feature engineering, preprocessing, training data management) - Solid software engineering fundamentals (testing, CI/CD, code review, production operations) Preferred Qualifications - 3+ years of full software development life cycle, including coding standards, code reviews, source control management, build processes, testing, and operations experience - Experience building RAG systems (vector databases, embedding models, retrieval pipelines) - Experience with agent/orchestration frameworks (LangChain, LangGraph, CrewAI, Bedrock Agents, or custom) - Experience with ML evaluation frameworks (especially for generative AI / LLM outputs) - Experience with time-series ML (forecasting, anomaly detection) - Experience with MLOps tooling (MLflow, SageMaker Pipelines, Step Functions, feature stores) - Experience with infrastructure-as-code (CDK, CloudFormation, Terraform) - Background in operational/infrastructure environments ## Description We are looking for an **AI/ML Engineer** to build, deploy, and operate the ML/AI systems that power the agentic decision intelligence workflow we are building. You are the person who takes a model from a notebook to production, builds the LLM integration layer, implements RAG pipelines, creates evaluation frameworks, and ensures our AI systems are reliable, observable, and continuously improving., Build and maintain LLM-powered components: structured reasoning chains, narrative generation, recommendation rationale - Implement and optimize prompt engineering pipelines with version control, A/B testing, and regression detection - Build RAG (Retrieval-Augmented Generation) systems that ground LLM outputs in operational data, historical playbooks, and domain knowledge - Build guardrails, validation layers, and output parsing for LLM responses. Optimize latency, cost, and quality trade-offs across LLM providers - Deploy ML models to production. Implement model monitoring: drift detection, performance degradation alerts, automated retraining triggers - Build A/B testing infrastructure for model experiments. Manage model versioning, rollback, and canary deployment. Ensure SLA compliance for inference latency and availability - Own the operational health of AI/ML services: monitoring, alarming, on-call, incident response, observability across the AI stack (prompt traces, latency histograms, token usage, error rates) - Write comprehensive tests (unit, integration, end-to-end) for ML pipelines ## Related Videos - [Infrastructure as Code: The Developer's Secret Weapon](https://www.wearedevelopers.com/videos/1221-infrastructure-as-code-the-developer-s-secret-weapon) - [Machine learning in the browser with TensorFlowjs](https://www.wearedevelopers.com/videos/155-machine-learning-in-the-browser-with-tensorflowjs) - [Bringing the power of AI to your application.](https://www.wearedevelopers.com/videos/1010-bringing-the-power-of-ai-to-your-application) - [DevOps for AI: running LLMs in production with Kubernetes and KubeFlow](https://www.wearedevelopers.com/videos/1222-devops-for-ai-running-llms-in-production-with-kubernetes-and-kubeflow) - [Implementing Feature Environments with AWS and Terraform](https://www.wearedevelopers.com/videos/531-implementing-feature-environments-with-aws-and-terraform) - [Machine Learning for Software Developers (and Knitters)](https://www.wearedevelopers.com/videos/154-machine-learning-for-software-developers-and-knitters) ## Related Articles - [How to Become an AI Engineer](https://www.wearedevelopers.com/magazine/331-how-to-become-an-ai-engineer) - [What Are Large Language Models?](https://www.wearedevelopers.com/magazine/304-what-are-large-language-models) - [From Prototype to Production: Build AI Agents with This Free 4-Course Learning Path](https://www.wearedevelopers.com/magazine/655-from-prototype-to-production-build-ai-agents-with-this-free-4-course-learning-path) - [Got AI ideas but no money? Here are 10 free ways to level up your AI skills with Google Cloud](https://www.wearedevelopers.com/magazine/600-got-ai-ideas-but-no-money-here-are-10-free-ways-to-level-up-your-ai-skills-with-google-cloud) - [MLOps – What’s the deal behind it?](https://www.wearedevelopers.com/magazine/125-mlops-what-s-the-deal-behind-it) - [MLOps And AI Driven Development](https://www.wearedevelopers.com/magazine/82-mlops-and-ai-driven-development)