# Full-Stack AI Engineer > Pavago Β· Argentina (Remote) Β· β€” Β· Posted 2026-05-17 **Workplace:** remote ## Description ### **πŸ€– Full-Stack AI Engineer (LLMs, AI Products, Full-Stack Development)** **Full-Time Remote | U.S. Business Hours** ### **πŸš€ About the Role** We’re hiring a highly technical and execution-focused **Full-Stack AI Engineer** to build and deploy production-ready AI-powered applications. This is not a research-only AI role. You’ll bridge: - full-stack software engineering, - AI/ML integration, - scalable infrastructure, - and user-facing product development to turn AI prototypes into reliable, real-world applications. You’ll work across: - backend systems, - frontend interfaces, - AI pipelines, - APIs, - vector databases, - and cloud infrastructure to deliver AI products that are scalable, secure, and user-friendly. If you enjoy: - building AI-powered SaaS products, - integrating LLMs into production systems, - and owning systems end-to-end, this role is a strong fit. ### **πŸ”₯ What You’ll Own** ### **AI Model Integration & LLM Applications** - Deploy and integrate: - OpenAI models - Hugging Face models - fine-tuned LLMs - PyTorch / TensorFlow models - Build scalable inference APIs using: - FastAPI - Flask - Node.js - Develop: - AI copilots - chatbots - AI assistants - intelligent workflows - Implement: - embeddings - vector search - RAG pipelines - semantic retrieval systems - Work with: - Pinecone - Weaviate - FAISS - vector databases ### **βš™οΈ Data Engineering & AI Pipelines** - Build ETL/ELT pipelines for: - text data - image data - structured datasets - Automate: - preprocessing - labeling - transformations - versioning - Orchestrate workflows using: - Airflow - Prefect - Dagster - Manage datasets inside: - Snowflake - BigQuery - Redshift ### **πŸ’» Full-Stack Application Development** - Build modern front-end interfaces using: - React - Next.js - Vue - Develop AI-powered user experiences including: - dashboards - assistants - analytics tools - AI workflows - Design backend services and microservices - Connect AI systems with business logic and APIs - Ensure applications are: - responsive - scalable - secure - production-ready ### **☁️ Infrastructure, Deployment & MLOps** - Containerize applications with Docker - Deploy services into Kubernetes environments - Build CI/CD pipelines for: - application releases - model deployments - infrastructure updates - Monitor: - latency - cost - uptime - model drift - Use tools such as: - MLflow - Weights & Biases - Vertex AI - SageMaker - Kubeflow ### **πŸ”’ Security & Reliability** - Implement: - secure APIs - authentication - permissions - access controls - rate limiting - Ensure compliance with: - GDPR - HIPAA - SOC 2 - Build reliable and fault-tolerant AI systems ### **🀝 Collaboration & Product Development** - Work closely with: - product teams - data scientists - engineering teams - Productionize AI prototypes into scalable systems - Translate product ideas into practical AI-powered features - Document systems for reproducibility and scalability ### **βœ… Required Experience & Skills** - 3+ years experience in: - software engineering - AI engineering - ML-integrated systems - Strong Python skills: - PyTorch - TensorFlow - AI tooling - Strong JavaScript / TypeScript skills: - React - Node.js - frontend frameworks - Experience deploying AI/ML models into production - Experience with: - APIs - vector databases - RAG pipelines - embeddings - Strong SQL and cloud data warehouse experience - Experience with Docker and cloud infrastructure ### **⭐ Nice-to-Have Experience** - AI-powered SaaS product development - LLM fine-tuning and custom model workflows - MLOps and model lifecycle management - Microservices and serverless architectures - Cost optimization for AI inference workloads - Experience with: - Vertex AI - SageMaker - Kubeflow - LangChain - AI agents - Startup or high-growth product experience ### **🧠 What Makes You a Strong Fit** - You can move from prototype β†’ production confidently - You understand both software engineering and AI systems deeply - You balance speed, scalability, and reliability - You are highly curious about emerging AI tools - You take ownership and execute independently - You care about real-world product impact β€” not just experimentation ### **πŸ“… What a Typical Day Looks Like** - Improve and deploy AI model APIs - Build frontend experiences for AI-powered workflows - Optimize vector search and retrieval systems - Maintain AI data pipelines and infrastructure - Monitor model latency, cost, and performance - Collaborate with product teams on AI feature prioritization - Debug production issues and improve reliability - Document systems and deployment workflows **In short:** You transform AI capabilities into scalable, production-ready applications that solve real business problems. ### **πŸ“Š Key Metrics for Success (KPIs)** - Successful AI feature deployments - Application uptime β‰₯ 99.9% - Inference latency under target thresholds - Stability and reliability of AI systems - Reduction in manual operational work - User adoption and satisfaction of AI features - Scalability and maintainability of infrastructure ### **🌟 Why This Role Stands Out** - High-impact AI product engineering role - Opportunity to work on real-world AI applications - Ownership across the full technical stack - Strong exposure to modern LLM infrastructure and tooling - Fast-paced engineering environment with meaningful product influence - Opportunity to shape AI architecture from the ground up ### **πŸ§ͺ Interview Process** - Initial Phone Screen - Video Interview with Pavago Recruiter - Technical Assessment - Client Interview(s) with Engineering Team - Offer & Background Verification ### **πŸ‘‰ Apply Now** If you: - love building AI-powered products, - can own systems end-to-end, - understand both full-stack engineering and applied AI, - and want to ship production-grade AI experiences, this role is a strong fit for you. ## Apply [Apply at Pavago](https://apply.workable.com/pavago/j/87446BB445/apply) --- Powered by [Workable](https://www.workable.com)