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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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