Senior AI Engineer
罗克星正在招募一个设计，构建和部署生产级人工智能系统的数据智能平台。他们是一支活跃且熟练的专业人员团队，知道如何创建杀手 AI 应用程序。我们的精细结构和远程团队意味着我们可以快速移动，同时还提供顶尖技术和设计。位置总结客户正在寻找一名高级人工智能工程师/高级机器学习工程师来设计，建造，部署和维护其数据智能平台中生产级人工资讯系统。这位候选人将领导开发基于 LLM 的应用程序，代理工作流程，检索增强生成系统，模型评估管道和可扩展的 AI 服务。理想的候选人结合了强大的机器学习专业知识与实际生产工程经验。该人员将拥有从概念到部署的复杂技术工作，指导其他工程师，并帮助定义建立可靠，可观察和安全的 AI 系统的最佳实践。基本责任 ·设计，构建和部署生产 GenAI 系统，包括 LLM 应用程序，机构工作流程 (agents workflows),RAG 管道和人工智能驱动的搜索能力。· 设计可扩展的 AI 服务，使用现代 ML 框架，模型服务工具，API,Docker,Kubernetes 和 CI/CD 管道。·开发并优化采集系统，使用嵌入式，向量数据库，语义搜索，重新排名和结构化的数据来源。· 用快速工程，监督细节调度，LoRA / QLoRA 或相关方法进行域特定应用案例的 LLM 精确调整，适应和评估。·建立自动评估框架来测量模型质量，快速性能，检索准确性，推理可靠性，延迟和成本。·实现 AI 系统的观察能力，包括追踪，记录，绩效监控，漂移检测和输出质量审查。· 将原型和研究概念转化为可靠的产品特性，可以在生产中扩展。· 与产品经理，数据工程师，后端工程师，分析师以及业务利益相关者合作，定义 AI 能力和技术交易。· 审查架构，提供技术指导，辅导年轻团队成员并促进强有力的工程实践。· 创建清晰的技术文档，实施计划，跑本和模型生命周期文件。所需资格 · 5年以上机器学习工程，人工智能工程，数据科学等领域的经验。
查看英文原文
Rockstar is recruiting for a data intelligence platform that designs, builds, and deploys production-grade AI systems. They are a team of dynamic and savvy professionals who know how to create killer AI applications. Our lean structure and remote team mean we can move fast while still delivering top-notch technology and design.
Position Summary
The client is seeking a Sr. AI Engineer/Sr. Machine Learning Engineer to design, build, deploy, and maintain production-grade AI systems across their data intelligence platform. This role will lead the development of LLM-powered applications, agentic workflows, retrieval-augmented generation systems, model evaluation pipelines, and scalable AI services.
The ideal candidate combines strong machine learning expertise with practical production engineering experience. This person will own complex technical work from concept through deployment, mentor other engineers, and help define best practices for building reliable, observable, and secure AI systems.
Essential Responsibilities
- Design, build, and deploy production GenAI systems, including LLM applications, agentic workflows, RAG pipelines, and AI-powered search capabilities.
- Architect scalable AI services using modern ML frameworks, model-serving tools, APIs, Docker, Kubernetes, and CI/CD pipelines.
- Develop and optimize retrieval systems using embeddings, vector databases, semantic search, reranking, and structured data sources.
- Fine-tune, adapt, and evaluate LLMs for domain-specific use cases using prompt engineering, supervised fine-tuning, LoRA / QLoRA, or related methods.
- Build automated evaluation frameworks to measure model quality, prompt performance, retrieval accuracy, reasoning reliability, latency, and cost.
- Implement observability for AI systems, including tracing, logging, performance monitoring, drift detection, and output-quality review.
- Translate prototypes and research concepts into reliable product features that can scale in production.
- Partner with product managers, data engineers, backend engineers, analysts, and business stakeholders to define AI capabilities and technical tradeoffs.
- Review architecture, provide technical guidance, mentor junior team members, and promote strong engineering practices.
- Create clear technical documentation, implementation plans, runbooks, and model lifecycle documentation.
Required Qualifications
- 5+ years of experience in machine learning engineering, AI engineering, data science engineering, or a related technical role.
- 2+ years of experience building or shipping production GenAI, LLM, or AI-powered systems.
- Advanced Python programming skills and experience building maintainable production software.
- Hands-on experience with PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn, or similar ML frameworks.
- Experience with LLM applications, RAG systems, embeddings, vector databases, prompt engineering, and model evaluation.
- Experience deploying AI / ML services using Docker, Kubernetes, CI/CD workflows, APIs, and cloud-native infrastructure.
- Strong understanding of classical machine learning, deep learning, NLP, information retrieval, and model validation.
- Ability to communicate complex AI concepts clearly to technical and non-technical stakeholders.
- Experience mentoring engineers, reviewing technical designs, or leading complex AI engineering initiatives.
Preferred Qualifications
- Advanced degree in Computer Science, Machine Learning, Artificial Intelligence, Data Science, or a related field.
- Experience with agent frameworks such as LangGraph, AutoGen, CrewAI, or similar tools.
- Experience with model-serving platforms such as vLLM, BentoML, Triton, Ray Serve, or similar systems.
- Familiarity with ML observability, experiment tracking, model monitoring, and prompt/version management tools.
- Experience with graph-based retrieval, knowledge graphs, multimodal models, large-scale data processing, or security-focused data products.
- Experience with infrastructure-as-code, workflow orchestration, model routing, caching, batching, or quantization.
Special Skills or Experience Required
- Proven experience building and deploying production GenAI systems, including LLM applications, agentic workflows, and RAG pipelines.
- Advanced Python and ML framework experience, including PyTorch, TensorFlow, Hugging Face Transformers, or similar tools.
- Experience with LLM fine-tuning, prompt engineering, embeddings, vector databases, semantic search, and model evaluation.
- Strong production engineering skills, including Docker, Kubernetes, CI/CD, model serving, observability, latency optimization, and technical leadership.
Success Measures
Success in this role will be measured by the delivery of reliable AI capabilities, improved model quality, reduced latency and cost, stronger evaluation coverage, improved observability, and the successful mentorship of other engineers. The role should help increase the speed and confidence with which the company can move AI features from prototype to production.
Originally posted on Himalayas