Full Stack AI Engineer
Posted 14 Sep 2026
Advertised as “Full-Stack AI Engineer”
BranchFactor Summary
The original posting with the fluff stripped out
- Management level
- Individual contributor
- Employment
- Remote
- Contract
- Full-time
No salary listed for this role.
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
At Pavago, one of our clients is hiring a 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’ll work across AI pipelines, backend systems, APIs, cloud infrastructure, and modern frontend frameworks to deliver scalable, production-ready AI applications. You’ll collaborate closely with engineering, product, and data teams to build intelligent automation, chat experiences, analytics tools, and AI-powered workflows.
Responsibilities
AI & LLM Development
- Integrate AI/ML models using OpenAI, Hugging Face, TensorFlow, PyTorch, or similar frameworks.
- Build AI inference APIs using FastAPI, Flask, Node.js, or similar technologies.
- Develop RAG pipelines using vector databases such as Pinecone, Weaviate, or FAISS.
- Implement embeddings, semantic search, and AI-powered workflows.
- Optimize inference performance, scalability, and cost.
Full-Stack Development
- Build frontend applications using React, Next.js, Vue, or similar frameworks.
- Develop backend services and APIs connecting AI models with business logic.
- Build AI-powered features including chatbots, copilots, dashboards, and automation tools.
- Design secure, scalable, and maintainable application architectures.
Data & Infrastructure
- Build ETL pipelines and data workflows using Airflow, Prefect, Dagster, or similar tools.
- Manage structured and unstructured datasets.
- Deploy AI services using Docker, Kubernetes, and cloud platforms such as AWS, Azure, or GCP.
- Build CI/CD pipelines and monitor system performance, reliability, and costs.
Security & Collaboration
- Implement secure authentication, authorization, and API protection.
- Maintain compliance with security and privacy standards where applicable.
- Collaborate with engineering, product, and data teams to deliver production-ready AI solutions.