Ready to take AI and Machine Learning from prototype to production? Join a forward-thinking technology team where you’ll build, deploy and scale ML, AI and GenAI solutions that deliver real business value.

WHAT YOU’LL DO

Productionise and deploy ML models & data science pipelines on Databricks
Build and support AI Agents, GenAI & RAG solutions
Develop robust MLOps / CI/CD pipelines with automated testing, monitoring and governance
Deploy and optimise Open Source LLMs & AI models on Azure Kubernetes Service (AKS)
Build scalable APIs & microservices to expose AI capabilities
Develop containerised solutions using Docker & Kubernetes
Monitor model performance, drift, reliability and production health
Partner with Data Scientists to turn prototypes into business-ready solutions
Troubleshoot, optimise and scale AI/ML platforms for performance, cost and reliability
Mentor junior engineers and contribute to AI/ML engineering best practices

Requirements

MUST-HAVE TECHNICAL SKILLS

Databricks
MLflow
Mosaic AI
Model Serving
AKS
Python
SQL
REST APIs
Docker
Kubernetes
CI/CD
MLOps
Machine Learning
GenAI
LLMs
RAG
Spark

QUALIFICATIONS

Degree in Computer Science, Engineering, Econometrics, Mathematical Statistics, Actuarial Science or a related field.

ADVANTAGEOUS CERTIFICATIONS

Microsoft Azure – AZ-104 / AZ-305 / AI-102
Databricks – Data Engineer / ML Engineer / GenAI Engineer
Kubernetes – CKA / CKAD
DevOps / MLOps / Platform Engineering
AWS or Google Cloud certifications

A hands-on Senior ML Engineer who understands how to take AI from development → deployment → production, with strong experience across Databricks, Azure, Kubernetes, MLOps and GenAI.

If you're passionate about building production-grade AI solutions at scale, this could be your next opportunity!

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