技术架构师 - 机器学习 - 生成式AI
Technical Architect - ML - GenAI
虽然技术是我们的业务核心，但全球多元的文化是我们的成功核心。我们热爱我们的员工，并自豪于为他们打造一个以透明、多元、诚信、学习和成长为基础的文化。
如果在鼓励你不仅在职业上而且在个人生活中创新和卓越的环境中工作吸引你，那么你在Quantiphi的职业生涯会非常愉快！
职位：Gen AI架构师（AWS）
经验级别：8年以上
工作地点：远程（美国）
职位概述：
我们正在寻找一位生成式AI架构师/负责人，使用AWS Bedrock和Agentcore设计和交付企业级GenAI解决方案。该职位专注于构建利用大型语言模型（LLMs）、检索增强生成（RAG）和代理AI工作流的可扩展应用。
理想的候选人应是一位亲力亲为的架构师，能够定义解决方案架构，指导团队，并积极参与开发，同时确保性能、可扩展性和成本效率。
主要职责：
- 使用AWS Bedrock和Agentcore设计和实现GenAI解决方案
- 为基于LLM的应用程序定义架构，包括RAG管道和代理工作流
- 开发和编排代理AI工作流，支持多步骤推理、工具使用和任务自动化
- 构建和管理RAG管道，包括嵌入、检索机制和向量数据库
- 通过API和后端服务将LLM功能集成到企业应用中
- 设计和优化提示工程策略，以提高准确性、相关性和性能
- 与结构化和非结构化数据源合作，实现基于知识的AI应用
- 确保模型评估、监控和优化，以降低延迟、成本并提升响应质量
- 与应用、数据和平台团队协作，实现端到端解决方案交付
- 定义安全、治理和负责任AI使用的最佳实践
- 解决生产环境中的GenAI系统问题
- 提供技术领导力并指导团队成员，同时保持亲力亲为
必备条件：
- 8年以上在AWS上实施和开发云ML解决方案的相关技术经验
- 熟悉AWS服务，有使用AWS Sagemaker和Bedrock的经验，能够利用不同类型的数据源、训练作业、实时和批量应用
- 使用LangChain、Strand Ag等框架设计和实现代理AI架构的经验
查看英文原文
While technology is the heart of our business, a global and diverse culture is the heart of our success. We love our people and we take pride in catering them to a culture built on transparency, diversity, integrity, learning and growth.
If working in an environment that encourages you to innovate and excel, not just in professional but personal life, interests you- you would enjoy your career with Quantiphi!
Role: Gen AI Architect (AWS)
Experience Level: 8+ Years
Work location: Remote (US)
Job Overview:
We are looking for a Generative AI Architect / Lead to design and deliver enterprise-grade GenAI solutions using AWS Bedrock and Agentcore. This role focuses on building scalable applications leveraging large language models (LLMs), retrieval-augmented generation (RAG), and agentic AI workflows.
The ideal candidate will be a hands-on architect who can define solution architecture, guide teams, and actively contribute to development while ensuring performance, scalability, and cost efficiency.
Key Responsibilities:
- Design and implement GenAI solutions using AWS Bedrock and Agentcore
- Define architecture for LLM-based applications, including RAG pipelines and agentic workflows
- Develop and orchestrate agentic AI workflows, enabling multi-step reasoning, tool usage, and task automation
- Build and manage RAG pipelines, including embeddings, retrieval mechanisms, and vector databases
- Integrate LLM capabilities into enterprise applications via APIs and backend services
- Design and optimize prompt engineering strategies for accuracy, relevance, and performance
- Work with structured and unstructured data sources to enable knowledge-driven AI applications
- Ensure model evaluation, monitoring, and optimization for latency, cost, and response quality
- Collaborate with application, data, and platform teams for end-to-end solution delivery
- Define best practices for security, governance, and responsible AI usage
- Troubleshoot and resolve issues in production GenAI systems
- Provide technical leadership and mentor team members while remaining hands-on
Must have:
- 8+ years of relevant hands-on technical experience implementing, and developing cloud ML solutions on AWS.
- Hands-on experience on AWS services. Proven experience using AWS Sagemaker and Bedrock leveraging different types of data sources, Training jobs, real-time and batch applications.
- Design and implement agentic AI architectures using frameworks such as LangChain, Strand Agents etc., enabling autonomous task planning, decision-making, and multi-step reasoning.
- Hands-on experience with Amazon AgentCore for building, deploying, and scaling production-grade agentic AI applications, including agent memory management, tool registry, and observability.
- Architect and deploy scalable AI solutions on AWS, leveraging services like Lambda, Bedrock, Step Functions, S3, API Gateway, and SageMaker.
- Proficiency in working with LLM APIs (e.g., Claude, Nova, and other third-party LLM providers), including API integration,and multi-model orchestration strategies.
- Hands-on experience fine-tuning or optimizing large language models (LLM)
- Familiarity with LLM tool use, prompt templating and context management.
- Strong expertise in Vector Databases, including indexing strategies, embedding generation, similarity search, and integration with RAG architectures.
- Model Evaluation & Optimization: Evaluate LLM's zero-shot and few-shot capabilities, fine-tuning hyperparameters, ensuring task generalization, and exploring model interpretability for robust web app integration.
- Develop and maintain Model Context Protocol (MCP) implementations to manage state, context windows, memory, and prompt orchestration across distributed agent systems.
- Experience with at least one of the workflow orchestration tools, Airflow, StepFunctions, SageMaker Pipelines, Kubeflow etc.
- Experience implementing secure, scalable APIs and integrating with 3rd-party data sources and tools
- Ability to collaborate with cross-functional teams such as Developers, QA, Project Managers, and other stakeholders to understand their requirements and implement solutions.
- Should have experience with Deep Learning Concepts - Transformers, BERT, Attention models, tokenization, embeddings.
Nice to have:
- Experience with software development, exposure to frontend backend frameworks and communication protocols
- Experience working on Infrastructure as Code (IaC) and CI/CD pipelines
- Experience with NLP concepts: syntactic/semantic analysis, NER etc.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
Originally posted on Himalayas