Full-Stack AI Engineer (AI)

TL;DR

Full-Stack AI Engineer (AI): Designing, building, and deploying AI-powered applications that bridge modern software engineering with applied machine learning with an accent on production-grade scalability and reliability. Focus on integrating LLMs, vector databases, and AI workflows into production environments while managing infrastructure and front-end interfaces.

Location: Must be based in South Africa or Kenya. Remote role with required alignment to U.S. client business hours.

Company

hirify.global is a recruitment partner connecting technical talent with global clients.

What you will do

• Deploy and integrate pre-trained and fine-tuned ML/LLM models using platforms like OpenAI and Hugging Face.
• Build scalable inference APIs and back-end services using frameworks like FastAPI or Node.js.
• Develop front-end interfaces in React or Next.js for AI-powered features.
• Design and optimize RAG pipelines and vector search systems.
• Build ETL pipelines for data ingestion and processing using orchestration tools.
• Maintain CI/CD pipelines and monitor production performance, latency, and infrastructure costs.

Requirements

• 3+ years of software engineering experience with exposure to AI/ML systems.
• Strong proficiency in Python and JavaScript/TypeScript.
• Hands-on experience with AI/ML frameworks like PyTorch, TensorFlow, or OpenAI APIs.
• Experience building scalable APIs and front-end applications using React or Next.js.
• Familiarity with Docker, Kubernetes, and CI/CD workflows.
• Must be based in South Africa or Kenya and available during U.S. business hours.

Nice to have

• Experience building and scaling AI-powered SaaS applications.
• Hands-on experience with embeddings, fine-tuning, and RAG pipelines.
• Familiarity with MLOps platforms like MLflow or Vertex AI.
• Knowledge of serverless architectures and microservices.

Culture & Benefits

• Opportunity to work on high-impact, production-grade AI systems.
• Exposure to modern AI infrastructure and MLOps practices.
• Collaborative environment working with product and data science teams.
• Focus on technical ownership and end-to-end system development.

Hiring process

• Initial phone screen and recruiter interview.
• Technical assessment involving ML model deployment and integration.
• Client interview(s) with the engineering team.