AWS AI 工程师 / USC 和 GC 候选人仅限申请
AWS AI Engineer / USC and GC Candidates can ONLY Apply
职位名称：AWS AI 工程师
地点：远程 美国
核心技能：
必须具备
AWS 服务 - Bedrock、SageMaker、ECS 和 Lambda
具备 AWS 组织和策略防护措施（SCP、AWS Config）的实际经验
有实现 RAG 架构并使用框架和 ML 工具的经验，如：Transformers、PyTorch、TensorFlow 和 LangChain
熟悉基础设施即代码的最佳实践，并有为 AWS 云构建 Terraform 模块的经验
微调大型语言模型，构建数据集并将 ML 模型部署到生产环境
Git 版本控制、代码审查和 DevOps 流程
加分项
AWS 或相关云认证
数据隐私和合规最佳实践（例如 PII 处理、安全模型部署）
数据科学背景或处理结构化/非结构化数据的经验
了解 FinOps 和云成本优化
Hugging Face、Node.js
策略即代码开发（如 Terraform Sentinel）
你将负责
总体职责：
我们正在招聘一位高级 AI AWS 工程师，该工程师实际构建过 AI/ML 应用程序，而不仅仅是阅读相关资料。该职位专注于检索增强生成（RAG）系统的实际开发、微调 LLM 以及 AWS 原生微服务，这些服务在企业环境中推动自动化、洞察力和治理。你将设计并交付可扩展、安全的服务，使大型语言模型真正投入运营——将其连接到实时基础设施数据、内部文档和系统遥测数据。
你将加入一个高影响力团队，在真实的企业环境中推动云原生 AI 的边界。这不是一个提示工程的沙盒或简历关键词陷阱。如果你只是在 BedRock 上浅尝辄止，在 LinkedIn 上提到过 RAG，或者只是读过向量搜索的内容——这可能不是适合你的职位。我们寻找的是那些在生产环境中架构、开发和支持 AI/ML 服务的候选人。
这是我们在公共云 AWS 工程团队中的一个实干家角色。我们不招聘只懂术语或参加会议的人。如果你曾构建过让你自豪的东西——尤其是涉及真实基础设施、真实数据和真实用户的东西——我们很乐意与你交流。如果你还在学习，那也很好——但这个职位不是入门级岗位，也不是纯理论性质的职位。
职责和义务：
实际操作 AWS（Lambda、Bedrock、SageMaker、Step Functions、DynamoDB、S3）。
负责 AWS 云服务的实施，包括基础设施、机器学习等。
查看英文原文
Job Title: AWS AI Engineer
Location: REMOTE USA
TOP SKILLS:
Must Have
AWS services- Bedrock, SageMaker, ECS and Lambda
Demonstrated experience with AWS organizations and policy guardrails (SCP, AWS Config)
Experience implementing RAG architectures and using frameworks and ML tooling like: Transformers, PyTorch, TensorFlow, and LangChain
Experience in Infrastructure as Code best practices and experience with building Terraform modules for AWS cloud
Fine-tuning large language models, building datasets and deploying ML models to production
Git-based version control, code reviews, and DevOps workflows
Nice To Have
AWS or relevant cloud certifications
Data privacy and compliance best practices (e.g., PII handling, secure model deployment)
Data science background or experience working with structured/unstructured data
Exposure to FinOps and cloud cost optimization
Hugging Face, Node.js
Policy as Code development (I.e. Terraform Sentinel)
What You’ll Do
GENERAL FUNCTION:
We are hiring a Sr AI AWS Engineer who has actually built AI/ML applications in cloud—not just read about them. This role centers on hands-on development of retrieval-augmented generation (RAG) systems, fine-tuning LLMs, and AWS-native microservices that drive automation, insight, and governance in an enterprise environment. You’ll design and deliver scalable, secure services that bring large language models into real operational use—connecting them to live infrastructure data, internal documentation, and system telemetry.
You’ll be part of a high-impact team pushing the boundaries of cloud-native AI in a real-world enterprise setting. This is not a prompt-engineering sandbox or a resume keyword trap. If you’ve merely dabbled in BedRock, mentioned RAG on LinkedIn, or read about vector search—this isn’t the right fit. We’re looking for candidates who have architected, developed, and supported AI/ML services in production environments.
This is a builder’s role within our Public Cloud AWS Engineering team. We aren’t hiring buzzword lists or conference attendees. If you’ve built something you’re proud of—especially if it involved real infrastructure, real data, and real users—we’d love to talk. If you’re still learning, that’s great too—but this isn’t an entry-level role or a theory-only position.
DUTIES AND RESPONSIBILITIES:
Hands-on role using AWS (Lambda, Bedrock, SageMaker, Step Functions, DynamoDB, S3).
Responsible for the implementation of AWS cloud services including infrastructure, machine learning, and artificial intelligence platform services.
Experience with LLM-based applications, including Retrieval-Augmented Generation (RAG) using LangChain and other frameworks.
Develop cloud-native microservices, APIs, and serverless functions to support intelligent automation and real-time data processing.
Collaborate with internal stakeholders to understand business goals and translate them into secure, scalable AI systems.
Own the software release lifecycle, including CI/CD pipelines, GitHub-based SDLC, and infrastructure as code (Terraform).
Support the development and evolution of reusable platform components for AI/ML operations.
Create and maintain technical documentation for the team to reference and share with our internal customers.
Excellent verbal and written communication skills in English.
SUPERVISORY RESPONSIBILITIES: None
MINIMUM KNOWLEDGE, SKILLS, AND ABILITIES REQUIRED:
7 years of hands-on software engineering experience with a strong focus on Python.
Experienced with AWS services, especially Bedrock or SageMaker
Familiar with fine-tuning large language models or building datasets and/or deploying ML models to production.
Demonstrated experience with AWS organizations and policy guardrails (SCP, AWS Config).
Solid experience implementing RAG architectures and LangChain.
Demonstrated experience in Infrastructure as Code best practices and experience with building Terraform modules for AWS cloud.
Strong background in Git-based version control, code reviews, and DevOps workflows.
Demonstrated success delivering production-ready software with release pipeline integration.
Nice-to-Haves:
AWS or relevant cloud certifications.
Policy as Code development (e.g., Terraform Sentinel).
Experience with Hugging Face, Golang, or Node.js.
Exposure to FinOps and cloud cost optimization.
Data science background or experience working with structured/unstructured data.
Awareness of data privacy and compliance best practices (e.g., PII handling, secure model deployment).
What You’ll Get
Competitive base salary
Medical, dental, and vision insurance coverage
Optional life and disability insurance provided
401(k) with a company match and optional profit sharing
Paid vacation time
Paid Bench time
Training allowance offering
You’ll be eligible to earn referral bonuses!
Originally posted on Himalayas