高级销售协调员，AI工厂
Lead GenAI Engineer
工作人员生成人工智能工程师背景 我们正在寻找一个工作人员生成 AI 工程师，设计，构建和运输生产级的生成 AI 和代理 AI 应用程序，以实现整个组织中的业务价值。这个角色集中在规模上建立 AI 应用程序和服务上。你将负责与领先的云基于 AI 服务集成的强大，安全和高度可扩展系统的建设。作为高级个人贡献者，你将带来深入技术专业知识，推动高质量的工程实践，并担任初中到中等级工程师的导师。你将与软件工程师，数据科学家和产品团队密切合作，将业务问题转化为生产级人工智能应用程序和服务。关键责任：应用和解决方案工程 ·设计，构建和运输生产级生成性和代理性的人工智能申请和服务，用于内部和外部用户 ·开发高质量的后端服务在 Python 中，具有强大的软件工程严格度围绕测试，性能和维护能力 ·通过设计和建造模块化的，抽象的组件和图书馆来完成所有你建立的重复使用和抽象化工作。· 构建使用 LangChain,LangGraph,Claude Agent SDK 和 Google ADK 等框架的多代理系统 · 与包括 Azure OpenAI,Google Vertex AI 和 AWS Bedrock 在内的领先 LLM 和基础模型 API 整合 ·设计并实施检索增强代 (RAG) 管道，包括文件摄入，零碎策略，嵌入，向量搜索和重新排名 ·建立清洁，经过良好测试的 RESTful 和/或 gRPC API,重点关注可靠性，安全性和性能 ·实现可观察性，追踪，评估，对生成和 Agentic AI 应用程序进行防护。· 部署和运营服务在主要云提供商 （例如 GCP,AWS 和 Azure）上利用管理服务 · 贡献平台架构决策和工程最佳实践 · 从原型开始通过生产部署，硬化和持续运行 · 导师和教练初级和中等级的工程师通过代码审查，建筑讨论和对配套编程 · 培养工程卓越，知识共享和不断改进文化 · 参加技术设计评估并促进团队成员专业成长
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Staff Generative AI Engineer
Background
We are looking for a Staff Generative AI Engineer to design, build, and ship production-grade Generative AI and Agentic AI applications that delivery business value across the organization. This role is focused on building AI applications and services at scale. You will be responsible for building robust, secure, and highly scalable systems that integrate with leading cloud-based AI services.
As a senior individual contributor, you will bring deep technical expertise, drive high-quality engineering practices, and serve as a mentor for junior to mid-level engineers. You will work closely with software engineers, data scientists, and product teams to translate business problems into production-grade AI applications and services.
Key Responsibilities
Application and Solution Engineering
- Design, build, and ship production-grade Generative and Agentic AI applications and services for internal and external users
- Develop high-quality backend services in Python, with strong software engineering rigor around testing, performance, and maintainability
- Champion reusability and abstraction in everything you build by designing and building modular, well-abstracted components and libraries
- Build multi-agent systems using frameworks such as LangChain, LangGraph, Claude Agent SDK and Google ADK
- Integrate with leading LLM and foundation model APIs, including Azure OpenAI, Google Vertex AI, and AWS Bedrock
- Design and implement Retrieval-Augmented Generation (RAG) pipelines, including document ingestion, chunking strategies, embeddings, vector search, and re-ranking
- Build clean, well-tested RESTful and/or gRPC APIs with a strong focus on reliability, security, and performance
- Implement observability, tracing, evaluation, guardrails for Generative and Agentic AI applications
- Deploy and operate services on major cloud providers (e.g., GCP, AWS, and Azure) leveraging managed services
- Contribute to platform architecture decisions and engineering best practices
- Take applications from prototype through production deployment, hardening, and ongoing operation
- Mentor and coach junior and mid-level engineers through code reviews, architecture discussions, and pair programming
- Foster a culture of engineering excellence, knowledge sharing, and continuous improvement
- Participate in technical design reviews and contribute to the professional growth of team members
Mentorship and Team Development
Required Qualifications
- 8-15 years of professional software engineering experience with at least 3-5 years of experience building AI/ML software products
- Bachelor's degree in Computer Science or a related field (Master's degree preferred)
- Strong proficiency in Python, with deep software engineering fundamentals (abstraction, modularity, system design, testing, performance)
- Hands-on experience building and shipping Generative and Agentic AI applications, including LLM integration, prompt engineering, and/or agentic workflows
- Practical experience integrating cloud-hosted LLM APIs such as Azure OpenAI, Vertex AI, and/or AWS Bedrock
- Experience with agent frameworks (e.g., LangChain, LangGraph, Google ADK, Claude Agent SDK) and vector databases (e.g., Pinecone, Weaviate, pgvector, Open Search, AlloyDB)
- Hands-on experience with Google Cloud Platform (GCP), Amazon Web Services (AWS), or Azure
- Strong understanding of API design, distributed systems, and cloud-native architecture
- Proven track record of taking systems from design through production deployment and operation
Preferred Qualifications
- Experience with containerization and orchestration (Docker, Kubernetes)
- Knowledge of Generative AI Risk Management frameworks (NIST RFM)
- Experience supporting developer platforms or internal tooling
- Experience writing design documents or helping define engineering standards
Originally posted on Himalayas