Full-Stack AI Engineer at Pavago

, , Argentina -

Full Time

Start Date

Immediate

Expiry Date

04 Aug, 26

Salary

0.0

Posted On

06 May, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, JavaScript, TypeScript, React, Node.js, PyTorch, TensorFlow, FastAPI, Vector Databases, RAG Pipelines, Docker, Kubernetes, LLM Integration, SQL, Next.js, MLOps

Industry

Staffing and Recruiting

Description

🤖 Full-Stack AI Engineer (LLMs, AI Products, Full-Stack Development) Full-Time Remote | U.S. Business Hours 🚀 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. 🔥 What You’ll OwnAI 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 ⚙️ 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 💻 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 🔒 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 ✅ 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 → 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 — not just experimentation 📅 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. 📊 Key Metrics for Success (KPIs) Successful AI feature deployments Application uptime ≥ 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 🧪 Interview Process Initial Phone Screen Video Interview with Pavago Recruiter Technical Assessment Client Interview(s) with Engineering Team Offer & Background Verification 👉 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.

Responsibilities

Build and deploy production-ready AI-powered applications by bridging full-stack software engineering with AI/ML integration. You will own the end-to-end development of AI pipelines, backend services, and frontend interfaces to turn prototypes into scalable products.