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
Full-Stack AI Engineer
Position Type: Full-Time, Remote
Working Hours: U.S. Business Hours
Location: Remote (LATAM, Eastern Europe, Pakistan, India, South Africa Preferred)
About the Role
We are hiring a highly skilled Full-Stack AI Engineer to build, deploy, and scale AI-powered applications that solve real business problems.
This role combines full-stack software engineering with applied AI/ML expertise. You will work across backend systems, AI pipelines, APIs, cloud infrastructure, and frontend applications to bring AI features from prototype to production.
The ideal candidate is both technically strong and product-minded — someone who can move quickly, build scalable systems, and turn modern AI capabilities into reliable, user-friendly products.
You will collaborate closely with engineering, product, and data teams to deliver AI-powered workflows, intelligent automation systems, chat experiences, analytics tools, and scalable machine learning infrastructure.
What You’ll Own
AI & LLM Integration
- Deploy and integrate AI/ML models using OpenAI, Hugging Face, TensorFlow, PyTorch, or similar frameworks
- • Build scalable APIs for AI inference using FastAPI, Flask, or Node.js
- • Develop retrieval-augmented generation (RAG) pipelines using Pinecone, Weaviate, FAISS, or vector databases
- • Implement embeddings, semantic search, and AI-powered workflows
- • Optimize inference performance, latency, and cost efficiency
Full-Stack Application Development
- Build frontend interfaces using React, Next.js, Vue, or modern JavaScript frameworks
- • Develop backend systems and APIs that connect AI models with business logic
- • Create user-facing AI features such as chatbots, copilots, dashboards, and automation tools
- • Ensure applications are responsive, secure, scalable, and production-ready
- • Build microservices and scalable backend architectures
Data Engineering & Pipelines
- Develop ETL pipelines for ingesting, cleaning, transforming, and managing datasets
- • Automate preprocessing, data labeling, and workflow orchestration using Airflow, Prefect, or Dagster
- • Manage structured and unstructured datasets in cloud environments
- • Maintain reliable pipelines for model training, fine-tuning, and evaluation
Infrastructure, DevOps & MLOps
- Containerize AI services using Docker and deploy applications using Kubernetes or cloud infrastructure
- • Build CI/CD pipelines for model deployments and application releases
- • Monitor model performance, drift, costs, and system reliability
- • Work with cloud platforms such as AWS, GCP, Azure, Vertex AI, or SageMaker
- • Improve scalability, uptime, and infrastructure efficiency
Security, Compliance & Reliability
- Implement secure API authentication, access control, and rate limiting
- • Ensure AI systems comply with GDPR, HIPAA, SOC 2, or related compliance requirements
- • Maintain monitoring, logging, and observability for production systems
- • Troubleshoot production incidents and optimize system reliability
Collaboration & Product Development
- Partner with product and data teams to define AI-powered product features
- • Translate AI prototypes into scalable production systems
- • Participate in sprint planning, technical discussions, and architecture decisions
- • Maintain clear technical documentation and reproducible workflows
What Makes You a Great Fit
- You are both a strong software engineer and a hands-on AI builder
- • You enjoy shipping AI-powered features that solve real-world business problems
- • You are comfortable moving from prototype to production independently
- • You think critically about scalability, performance, cost, and usability
- • You stay current with rapidly evolving AI tools, frameworks, and infrastructure
- • You communicate clearly and collaborate effectively across technical and non-technical teams
Required Experience & Skills
- 3+ years of software engineering experience with AI/ML exposure
- • Strong proficiency in Python and JavaScript/TypeScript
- • Experience with AI/ML frameworks such as PyTorch or TensorFlow
- • Experience deploying ML or LLM systems into production environments
- • Strong frontend experience with React, Next.js, or Vue
- • Experience building APIs and backend services
- • Strong SQL skills and experience with cloud data platforms
- • Familiarity with Docker, CI/CD pipelines, and cloud deployments
Preferred Experience
- Experience building AI-powered SaaS platforms or automation products
- • Experience with LLM fine-tuning, embeddings, and RAG systems
- • Familiarity with vector databases and semantic search infrastructure
- • Experience with MLOps tools such as MLflow, Kubeflow, Vertex AI, or SageMaker
- • Knowledge of microservices, serverless architectures, and distributed systems
- • Experience optimizing inference cost and performance at scale
What a Typical Day Looks Like
A Full-Stack AI Engineer’s day revolves around building production-ready AI systems and scalable applications. You will:
• Build and optimize AI-powered APIs and backend services
• Develop frontend interfaces for AI-driven experiences and workflows
• Maintain data pipelines and model integration systems
• Monitor production environments for performance, uptime, and cost efficiency
• Collaborate with engineering and product teams to prioritize and ship AI features
• Troubleshoot system bottlenecks and continuously improve scalability and reliability
In short: you help transform AI capabilities into scalable, production-grade products that drive real business impact.
Key Metrics for Success (KPIs)
- Successful deployment of AI-powered features on schedule
- • Application uptime and infrastructure reliability maintained at high standards
- • Fast and stable inference performance for production endpoints
- • Reduction in manual workflows through AI automation
- • Strong adoption and usage of AI-powered product features
- • Scalable, maintainable, and cost-efficient system architecture
Interview Process
- Initial Phone Screen
• Video Interview with Pavago Recruiter
- Technical Assessment (AI API + Full-Stack Integration Exercise)
- • Client Interview with Engineering Team
• Offer & Onboarding
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Originally posted on Himalayas