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!