Full-Stack AI Engineer 12 views

Remote Full-Stack AI Engineer Jobs 2026

An exciting career opportunity has emerged for a Full-Stack AI Engineer to join a dynamic team working remotely during U.S. client business hours. Facilitated by Pavago, this full-time role bridges the gap between software engineering and applied machine learning, ensuring that cutting-edge AI models are integrated into production systems that are scalable, reliable, and user-friendly.

This role is perfect for a strong coder who is comfortable building prototypes and scaling them to production-grade systems. If you are an analytical problem solver who stays current with emerging AI/LLM tools and frameworks, this is your chance to turn AI concepts into practical, business-driven solutions.

Job Overview and Details

Below is a quick overview of the job specifications, working hours, and technical requirements:

Key Responsibilities

As a Full-Stack AI Engineer, your daily tasks will revolve around connecting models to real-world applications. Core duties include:

• AI Model Integration: Deploy ML/LLM models (OpenAI, Hugging Face), wrap them in APIs (FastAPI, Node.js), and implement vector search integrations (Pinecone, FAISS) for RAG.
• Data Engineering & Pipelines: Build ETL pipelines, automate data preprocessing with Airflow/Prefect, and manage datasets in Snowflake, BigQuery, or Redshift.
• Full-Stack Development: Build responsive front-end UIs in React, Next.js, or Vue to surface AI features (chatbots, dashboards) and design robust back-end microservices.
• Infrastructure & MLOps: Containerize services with Docker, deploy to Kubernetes, automate CI/CD, and monitor latency/model drift with MLflow or Weights & Biases.
• Security & Compliance: Ensure data privacy compliance (GDPR, SOC 2) and implement rate limiting and access controls.

Ideal Experience & Skills

While the minimum requirement is 3 years of software engineering with AI exposure, the ideal candidate will also bring:

• Proven experience building and scaling AI-powered SaaS products.
• Hands-on expertise with LLM fine-tuning, embeddings, and RAG pipelines.
• Deep knowledge of MLOps practices (Kubeflow, Vertex AI, SageMaker).
• Familiarity with serverless architectures and cost-optimized inference.

Interview Process

The recruitment process is designed to thoroughly assess your technical and collaborative skills:

1. Initial Phone Screen
2. Video Interview with a Pavago Recruiter
3. Technical Assessment (e.g., deploying a small ML model with API endpoints and basic front-end integration)
4. Client Interview(s) with the Engineering Team
5. Offer & Background Verification