全栈AI工程师
Full-Stack AI Engineer
全栈人工智能工程师
职位类型：全职，远程
工作时间：美国商务时间
地点：远程（拉美、东欧、巴基斯坦、印度、南非优先）
职位简介
我们正在招聘一名技术能力出色的全栈人工智能工程师，负责构建、部署和扩展解决实际业务问题的AI驱动应用。
该职位结合全栈软件工程与应用AI/ML的专业知识。您将跨后端系统、AI流程、API、云基础设施和前端应用工作，将AI功能从原型带入生产环境。
理想的候选人既具备扎实的技术能力，又具有产品思维——能够快速行动，构建可扩展的系统，并将现代AI能力转化为可靠、用户友好的产品。
您将与工程、产品和数据团队紧密合作，交付AI驱动的工作流程、智能自动化系统、聊天体验、分析工具和可扩展的机器学习基础设施。
您将负责的内容
AI与大语言模型集成
- 使用OpenAI、Hugging Face、TensorFlow、PyTorch或其他类似框架部署和集成AI/ML模型
- • 使用FastAPI、Flask或Node.js构建可扩展的AI推理API
- • 使用Pinecone、Weaviate、FAISS或向量数据库开发检索增强生成（RAG）流程
- • 实现嵌入、语义搜索和AI驱动的工作流程
- • 优化推理性能、延迟和成本效率
全栈应用开发
- 使用React、Next.js、Vue或现代JavaScript框架构建前端界面
- • 开发连接AI模型与业务逻辑的后端系统和API
- • 创建面向用户的AI功能，如聊天机器人、协作者、仪表盘和自动化工具
- • 确保应用响应迅速、安全、可扩展且适合生产环境
- • 构建微服务和可扩展的后端架构
数据工程与流程
- 开发ETL流程用于数据摄取、清洗、转换和管理
- • 使用Airflow、Prefect或Dagster自动化预处理、数据标注和流程编排
- • 在云环境中管理结构化和非结构化数据集
- • 维护可靠的流程用于模型训练、微调和评估
基础设施、DevOps与MLOps
- 使用Docker对AI服务进行容器化，并使用Kubernetes或云基础设施部署应用
- • 构建模型部署和应用发布的CI/CD流程
- • 监控模型性能、漂移、成本和系统指标
查看英文原文
Job Title: Full-Stack AI Engineer
Position Type: Full-Time, Remote
Working Hours: U.S. client business hours (with flexibility for deployments, experimentation cycles, and sprint schedules)
About the Role
Our client is seeking a highly skilled Full-Stack AI Engineer to design, build, and deploy scalable AI-powered applications that solve real-world business problems.
This role bridges software engineering with applied machine learning, combining front-end development, back-end systems, AI model integration, and cloud infrastructure into production-ready applications. You will work across the full product lifecycle — from experimentation and prototyping to deployment, optimization, and monitoring.
The ideal candidate is both technically strong and execution-focused, capable of building AI-driven systems that are scalable, reliable, performant, and user-friendly.
Responsibilities
AI Model Integration & LLM Systems
- Deploy and integrate pre-trained and fine-tuned ML / LLM models using OpenAI, Hugging Face, TensorFlow, PyTorch, or similar frameworks
- • Build scalable AI inference APIs using FastAPI, Flask, Node.js, or similar technologies
- • Implement retrieval-augmented generation (RAG) pipelines using vector databases such as Pinecone, Weaviate, Chroma, or FAISS
- • Optimize prompt engineering, embeddings, and AI workflows for performance, accuracy, and cost efficiency
Full-Stack Application Development
- Build responsive front-end applications using React, Next.js, Vue, or similar frameworks
- • Develop back-end services and APIs connecting AI systems to business workflows and user-facing applications
- • Design scalable architectures for chatbots, AI assistants, analytics dashboards, search systems, and workflow automation tools
- • Ensure applications are intuitive, secure, responsive, and production-ready
Data Engineering & Pipeline Development
- Build ETL/ELT pipelines for ingesting, cleaning, transforming, and processing structured and unstructured datasets
- • Automate data preprocessing, versioning, labeling, and pipeline orchestration using Airflow, Prefect, Dagster, or similar tools
- • Store and manage datasets within cloud warehouses such as Snowflake, BigQuery, or Redshift
- • Maintain reliable data flows supporting training, inference, analytics, and AI operations
Infrastructure, Deployment & MLOps
- Containerize AI services using Docker and deploy workloads to Kubernetes or cloud-native environments
- • Build and maintain CI/CD pipelines for AI model updates and application releases
- • Monitor inference latency, application performance, costs, and model drift using MLflow, Weights & Biases, Prometheus, or custom dashboards
- • Support scalable and reliable cloud infrastructure on AWS, GCP, or Azure
Security & Compliance
- Ensure AI systems comply with GDPR, HIPAA, SOC 2, or relevant privacy/security standards
- • Implement authentication, access control, rate limiting, and secure API practices
- • Protect user data and AI workflows using modern security standards and best practices
Collaboration & Product Development
- Collaborate with product managers, designers, and data scientists to prioritize impactful AI features
- • Translate prototypes into production-grade systems with scalable architecture and maintainable code
- • Participate in sprint planning, architecture discussions, code reviews, and technical documentation
- • Maintain clear documentation to support reproducibility, onboarding, and long-term maintainability
What Makes You a Perfect Fit
- Strong software engineer with deep curiosity around AI/ML systems and emerging technologies
- • Comfortable moving quickly from prototype to production-grade deployment
- • Analytical and solutions-oriented with strong debugging and optimization skills
- • Able to balance performance, scalability, usability, and operational cost
- • Collaborative communicator who works effectively across technical and non-technical teams
Required Experience & Skills
- 3+ years of professional software engineering experience with AI/ML exposure
- • Strong proficiency in Python and JavaScript/TypeScript
- • Experience with AI/ML frameworks such as PyTorch, TensorFlow, LangChain, or Hugging Face
- • Experience deploying AI or ML models into production systems
- • Strong front-end experience with React, Next.js, or Vue
- • Strong SQL skills and experience with cloud data warehouses
- • Familiarity with REST APIs, microservices, and distributed systems
- • Experience with Docker, CI/CD workflows, and cloud infrastructure
Preferred Experience & Skills
- Experience building and scaling AI-powered SaaS applications
- • Strong understanding of embeddings, vector databases, and RAG architectures
- • Experience with LLM fine-tuning, evaluation, and prompt optimization
- • Familiarity with MLOps tools such as MLflow, Kubeflow, Vertex AI, SageMaker, or Weights & Biases
- • Experience with serverless architectures and cost-optimized inference systems
- • Background in SaaS, automation platforms, analytics systems, or AI-driven products
What Does a Typical Day Look Like?
A Full-Stack AI Engineer’s day revolves around transforming AI capabilities into scalable, production-ready applications. You will:
- Review and optimize AI model APIs for latency, accuracy, and reliability
- • Build front-end interfaces that expose AI-driven functionality to end users
- • Maintain and improve data pipelines supporting AI systems and analytics
- • Deploy updates through CI/CD workflows and monitor production performance
- • Collaborate with product and data science teams on AI feature prioritization
- • Debug infrastructure, inference, or workflow issues impacting system performance
- • Document architectures, workflows, and deployment processes for maintainability and scaling
In essence: you ensure AI systems move beyond prototypes into secure, scalable, reliable, and impactful production applications.
Key Metrics for Success (KPIs)
- Successful deployment of AI features aligned with sprint timelines
• Application uptime ≥ 99.9%
- Inference latency maintained below target thresholds
- • Reduction in manual workflows through AI automation
- • Stable model performance and minimized drift or degradation
- • Positive adoption and engagement with AI-powered features
- • Scalable, maintainable, and cost-efficient AI infrastructure
Interview Process
- Initial Phone Screen
• Video Interview with Pavago Recruiter
- Technical Assessment (e.g., deploy an ML model with API + front-end integration)
- • Client Interview(s) with Engineering / Product Teams
• Offer & Background Verification
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Originally posted on Himalayas