About The Role
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.
We are looking for an AI Engineer who can take models from experimentation to production and build the tooling that supports them. You'll work at the intersection of software engineering and machine learning, building the systems that give AI real-world impact.
Who You Are
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.
MUST HAVES
- 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
What Success Looks Like
- Your code is reliable in production, well-tested, monitored, and easy for teammates to extend
- You reduce time-to-deployment by building clean, reusable infrastructure that data scientists can actually use
- You proactively spot production issues before clients do, setting up the right alerts, logs, and dashboards
- You work fluidly with data scientists, translating model requirements into solid engineering with minimal friction
- You contribute to internal tooling and frameworks that make future engagements faster and higher quality
- You are a self-starter who picks up new tools and frameworks quickly and brings them into the team's workflow when they add value
