AmazonPosted 4w ago

AI/ML Engineer, Amazon Global Data Center Ops Central Insight and Analytics Team

AI/ML Engineer, Amazon Global Data Center Ops Central Insight and Analytics Team at Amazon scores 99 out of 100 on AI centrality, which makes it AI Level 4 of 4 (Builds AI) on this board. The level measures how much of the work is AI, not seniority.

US, WA, Seattlemidfull-time$144k-$194k

AI in this role

langchainlanggraphcrewaibedrockmlflowsagemaker

prompt-engineeringragllm-integrationml-ops

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.

This is a hands-on engineering role with deep ML/AI focus — you write production code that runs AI systems, not research papers. If you love the intersection of ML infrastructure, LLM applications, and production engineering, this role is for you.

Key job responsibilities
- 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

Basic qualifications

- 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

Amazon is an equal opportunity employer and does not discriminate on the basis of protected veteran status, disability, or other legally protected status.

Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.

The base salary range for this position is listed below. Your Amazon package will include sign-on payments and restricted stock units (RSUs). Final compensation will be determined based on factors including experience, qualifications, and location. Amazon also offers comprehensive benefits including health insurance (medical, dental, vision, prescription, Basic Life & AD&D insurance and option for Supplemental life plans, EAP, Mental Health Support, Medical Advice Line, Flexible Spending Accounts, Adoption and Surrogacy Reimbursement coverage), 401(k) matching, paid time off, and parental leave. Learn more about our benefits at https://amazon.jobs/en/benefits.

USA, WA, Seattle - 143,700.00 - 194,400.00 USD annually

Prepare for this job

A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.

Skills and AI tools this role asks for

Prompt EngineeringRagLlm IntegrationMl OpsLangChainLangGraphCrewAIBedrock

Questions you could be asked

How do you structure and test a prompt to get consistent output from a language model?
How would you design a retrieval step so the model answers from real data instead of guessing?
How have you integrated a large language model into a production application?
How do you monitor a model once it's live, and how do you know it needs retraining?
Walk me through how you've used LangChain in your day-to-day work.

Adapt your resume

List these exact terms on your resume: Prompt Engineering, Rag, Llm Integration, Ml Ops, and LangChain. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.

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