Full-Stack AI Engineer
- Compensation
- Not disclosed
- Location
- South Africa
- Work setup
- Remote
- Seniority
- Staff
- Employment
- Full time
- Experience
- 3+ years
- Category
- Fullstack
- Posted
Skills
Nice to have
- Experience building AI-powered SaaS platforms.
- Experience with:
- Experience using:
- Familiarity with:
- Experience optimizing inference latency and cloud infrastructure costs.
- Knowledge of AI observability, evaluation frameworks, and model drift monitoring.
Full description
Full-Stack AI Engineer – RemoteAI Engineering | LLMs | Python | React | MLOps | Cloud InfrastructurePosition Type: Full-Time, Remote
Working Hours: U.S. Client Business Hours (with flexibility for sprint planning, deployments, and experimentation cycles)
About the Role
At Pavago, one of our clients is hiring a Full-Stack AI Engineer to design, build, and deploy production-ready AI applications that combine modern software engineering with applied artificial intelligence.
This is a highly technical, hands-on role where you’ll build intelligent products from end to end—integrating Large Language Models (LLMs), machine learning models, vector databases, cloud infrastructure, and modern web applications into scalable production systems.
You’ll collaborate closely with product managers, data scientists, and engineering teams to develop AI-powered solutions that automate workflows, improve user experiences, and create measurable business impact.
If you’re passionate about shipping AI products—not just experimenting with models—this role is built for you.
What You’ll Own
AI Application Development
- Build and deploy AI-powered applications using modern software engineering best practices.
- Integrate LLMs and machine learning models into production environments.
- Develop intelligent features including:
- AI chatbots
- Semantic search
- Document intelligence
- AI copilots
- Workflow automation
- Build scalable APIs that expose AI capabilities to applications.
LLMs, RAG & AI Integration
- Integrate models using:
- OpenAI
- Hugging Face
- PyTorch
- TensorFlow
- Build Retrieval-Augmented Generation (RAG) pipelines.
- Implement semantic search using vector databases including:
- Pinecone
- Weaviate
- FAISS
- ChromaDB
- Optimize prompt engineering and inference workflows.
- Monitor model accuracy, latency, and production performance.
Data Engineering & AI Pipelines
- Build ETL pipelines for structured and unstructured data.
- Automate:
- Data ingestion
- Cleaning
- Validation
- Versioning
- Manage workflows using:
- Airflow
- Prefect
- Dagster
- Work with cloud data warehouses including:
- BigQuery
- Snowflake
- Amazon Redshift