高级AI工程师
Senior AI Engineer
关于TensorOps
TensorOps是一家专注于AI的咨询公司，连接战略与执行，我们为企业的客户设计并交付生产级别的AI系统，从财富500强公司到快速发展的独角兽企业。我们的工作涵盖代理AI、LLM微调、RAG系统和ML驱动的产品，部署在AWS、GCP和Azure上。
我们已经交付了每天影响2亿+终端用户的AI系统，与11家独角兽企业和纳斯达克上市的公司（包括Notion、ServiceNow、JFrog、Seeking Alpha、Armis和GoCardless）合作，并且在两个月内将95%的验证想法投入生产。我们是Google Cloud、AWS和Cloudflare的合作伙伴，我们是完全远程办公的。
关于职位
我们正在招聘一名高级机器学习工程师，以在客户项目中提供技术方向，并指导不断壮大的初级机器学习工程师团队。你将直接与客户合作，将AI系统从原型开发到生产级部署。
在这个职位中，你将：
- 为企业的客户设计、构建和部署生产级的机器学习和基于LLM的系统（RAG、代理流程、微调、嵌入）
- 全程负责技术交付：从架构和原型设计到部署、监控和迭代
- 直接与客户的工程和产品团队合作，将业务需求转化为可实现的技术解决方案
- 指导和支持团队中的其他机器学习工程师——代码审查、技术指导和知识分享
- 在团队成长过程中帮助制定内部最佳实践、工具和技术标准
- 在客户会议、研讨会中（可选）在行业会议上代表TensorOps进行技术交流
你将加入一个支持性强、发展迅速的团队，重视自主性、开放沟通和持续学习。
要求
- 5年以上机器学习、AI工程或相关领域的专业经验
- 熟练使用Python，编写干净、高效、文档齐全、生产质量的代码
- 有独立设计、训练、优化和部署机器学习模型的经验（例如PyTorch、TensorFlow、Scikit-learn）
- 有构建生成式AI和LLM系统的经验：RAG管道、聊天机器人架构和应用，使用LangChain等工具
- 熟悉MLOps和生产级机器学习实践：模型版本控制、监控、ML工作流的CI/CD
- 有在AWS、GCP或Azure上部署和扩展机器学习系统的经验
- 强大的性能优化和调试技能（诊断复杂问题）
查看英文原文
About TensorOps
TensorOps is a boutique AI consultancy that bridges strategy and execution, we design and ship production-grade AI systems for enterprise clients, from Fortune 500 companies to fast-growing unicorns. Our work spans agentic AI, LLM fine-tuning, RAG systems, and ML-driven products, deployed on AWS, GCP, and Azure.
We've shipped AI systems impacting 200M+ end users daily, partnered with 11 unicorns and NASDAQ-listed companies (including Notion, ServiceNow, JFrog, Seeking Alpha, Armis, and GoCardless), and get 95% of validated ideas into production within two months. We're Google Cloud, AWS, and Cloudflare partners, and we're 100% remote by design.
About the role
We're hiring a Senior ML Engineer to contribute to technical direction across client engagements and mentor a growing team of junior ML engineers. You'll work directly with clients, taking AI systems from prototype to production-grade deployment.
In this role, you will:
- Design, build, and deploy production ML and LLM-based systems (RAG, agentic workflows, fine-tuning, embeddings) for enterprise clients
- Own technical delivery end-to-end: from architecture and prototyping to deployment, monitoring, and iteration
- Work directly with client engineering and product teams to translate business needs into scoped, shippable technical solutions
- Mentor and support other ML engineers on the team — code reviews, technical guidance, and knowledge sharing
- Help shape internal best practices, tooling, and technical standards as the team grows
- Represent TensorOps technically in client conversations, workshops, and (optionally) at industry conferences
You’ll be part of a supportive, fast-growing team that values autonomy, open communication, and continuous learning.
Requirements
- 5+ years of professional experience in Machine Learning, AI Engineering, or a related role
- Strong hands-on skills in Python, writing clean, efficient, well-documented, production-quality code
- Proven experience designing, training, optimizing, and deploying ML models independently (e.g., PyTorch, TensorFlow, Scikit-learn)
- Experience building GenAI & LLM systems: RAG pipelines, chatbot architectures, and applications using tools like LangChain
- Familiarity with MLOps & production ML practices: model versioning, monitoring, CI/CD for ML workflows
- Experience deploying and scaling ML systems on AWS, GCP, or Azure
- Strong performance optimization and debugging skills (diagnosing complex issues and improving system reliability and efficiency)
- Experience working with stakeholders or clients is a plus
What We Offer
- 100% Remote Work: no mandatory office days, work from wherever
- Funded certifications: fully paid AWS and GCP professional certifications
- Dynamic, High-Impact Projects: Work on cutting-edge ML and GenAI solutions across diverse industries
- International Clients: Collaborate with global organizations and solve real-world challenges at scale
- Urban Sports Club Membership: Supporting your physical and mental wellbeing
- Monthly Bolt Credits: For rides
- Company Events & Offsites: Regular team gatherings to connect, collaborate, and celebrate
Originally posted on Himalayas