Unibrix

We are seeking a highly skilled AI/ML Engineer to design, build, and scale production-grade AI systems. The ideal candidate combines expertise in AI/ML, backend engineering, cloud infrastructure, and MLOps to deliver end-to-end AI solutions powered by LLMs, RAG architectures, and intelligent agents.

You will play a key role in developing scalable AI products, optimising inference performance, building AI platforms, and integrating cutting-edge AI capabilities into business applications.

Key Responsibilities

• Design and develop scalable AI/ML solutions for production environments.
• Build and deploy LLM-powered applications using OpenAI, Claude, Gemini, and Hugging Face ecosystems.
• Develop Retrieval-Augmented Generation (RAG) systems and agentic workflows.
• Create and maintain AI APIs and backend services using Python and FastAPI.
• Design data pipelines, vector search architectures, and AI orchestration workflows.
• Implement MLOps practices, including model deployment, monitoring, retraining, and observability.
• Optimise AI systems for latency, scalability, reliability, and cost efficiency.
• Collaborate with product, engineering, and data teams to deliver AI-driven features.
• Work with cloud-native infrastructure and containerised environments.
• Mentor junior engineers and contribute to architectural decisions.

Required Qualifications

• 5+ years of software engineering or machine learning experience.
• Strong proficiency in Python.
• Hands-on experience building and deploying production AI systems.
• Deep understanding of machine learning fundamentals, deep learning, NLP, and transformers.
• Experience with LLM technologies and frameworks:
  • OpenAI API
  • Claude API
  • Gemini
  • Hugging Face
  • LangChain
  • LangGraph
  • LlamaIndex
• Strong knowledge of:
  • RAG architectures
  • Vector databases
  • Embeddings
  • Agentic workflows
  • Prompt engineering
• Experience building REST APIs and backend services.
• Proficiency with Docker and cloud platforms (AWS, Azure, or GCP).
• Experience with SQL databases and data processing frameworks.

Preferred Qualifications

• Kubernetes and container orchestration experience.
• Infrastructure as Code using Terraform or similar tools.
• MLOps platforms such as MLflow, Kubeflow, or Airflow.
• Experience with Spark and large-scale data processing.
• Knowledge of Redis, Kafka, RabbitMQ, or real-time systems.
• Experience with AI observability, monitoring, and model lifecycle management.
• Exposure to enterprise AI integrations and AI governance.

Technical Stack

Languages

• Python
• SQL

Backend

• FastAPI
• Flask
• REST APIs
• GraphQL

AI/ML

• OpenAI
• Claude
• Gemini
• Hugging Face
• LangChain
• LangGraph
• LlamaIndex
• RAG
• AI Agents

Data

• PostgreSQL
• MySQL
• Pinecone
• Weaviate
• Qdrant
• ChromaDB

Cloud & DevOps

• AWS / Azure / GCP
• Docker
• Kubernetes
• Terraform
• GitHub Actions / GitLab CI