Calibrax AI delivers end-to-end AI solutions, from strategy and model development to deployment, integration, and long-term optimization.
Our engineers build the systems that make AI actually work at scale: pipelines, APIs, integrations, and the infrastructure that keeps everything running.
We work across a diverse range of clients and enterprises, in environments where reliability and performance are not optional.
You will take models from experimentation to production and build the tooling that supports them.

You write code that other engineers respect, clean, tested, and designed with the next person in mind.
You understand ML well enough to work closely with data scientists, translate their requirements into reliable systems, and improve the infrastructure around their models without breaking what works.
You're a builder. Whether it's an API wrapper around a fine-tuned LLM, a real-time feature pipeline, or an MLOps workflow that cuts retraining time in half, you like shipping things and you like doing it well. You care about observability, performance, and maintainability in equal measure.
You're curious about what's next. LLMs, vector search, agentic systems, you follow where the field is going and you're eager to get your hands on what's emerging.

3+ years of software engineering experience, with at least 1 year working on ML or AI systems
Strong proficiency in Python; experience with FastAPI, Flask, or similar for API development
Experience building and maintaining data pipelines and ETL workflows
Hands-on experience with cloud platforms (AWS, GCP, or Azure) and containerisation (Docker, Kubernetes)
Familiarity with MLOps practices: experiment tracking, model versioning, CI/CD for ML
Experience working with LLMs, API integration, prompt management, RAG pipelines, or fine-tuning
Strong understanding of software engineering fundamentals: testing, version control, code review

AI Engineer

Key skills