Senior Machine Learning Engineer - AI / GenAI

Role Overview

We are looking for an experienced Senior Machine Learning Engineer to design, build, deploy and support enterprise-scale Machine Learning, Artificial Intelligence and Generative AI solutions.

The role is strongly focused on the productionisation and operationalisation of AI and ML solutions, including machine learning models, GenAI applications, AI agents, Retrieval-Augmented Generation (RAG) solutions and reusable ML platforms.

The successful candidate will work extensively across Databricks, Azure Kubernetes Service (AKS), Kubernetes, Docker, MLflow and MLOps environments, partnering closely with Data Scientists, Cloud Engineers, Platform Teams, Security Teams and business stakeholders.

This is an engineering-focused role requiring strong experience taking AI and Machine Learning solutions from prototype through deployment, scaling, monitoring and production support.

Key Responsibilities

• Design, build, deploy and support production-grade Machine Learning and AI solutions.

• Productionise Machine Learning models and Data Science pipelines using Databricks.

• Develop and deploy Generative AI applications, AI agents and RAG solutions.

• Build reusable Machine Learning pipelines and frameworks using MLOps principles.

• Implement CI/CD, automated testing, model monitoring, governance and deployment automation.

• Deploy and optimise open-source Machine Learning and Large Language Models within Azure Kubernetes Service (AKS).

• Develop and support REST APIs and microservices that expose AI and Machine Learning capabilities to enterprise applications.

• Build scalable containerised solutions using Docker and Kubernetes.

• Implement and maintain Databricks Workflows, MLflow, Model Serving and Mosaic AI solutions.

• Monitor models for performance degradation, model drift, reliability and operational health.

• Troubleshoot production issues across models, ML pipelines, APIs, GenAI applications and supporting infrastructure.

• Optimise AI and ML platforms for performance, scalability, reliability and cost efficiency.

• Collaborate with Data Scientists to convert models and prototypes into business-ready production solutions.

• Partner with Cloud, Infrastructure, Security and Platform Engineering teams to ensure solutions comply with enterprise architecture and security standards.

• Contribute to reusable engineering frameworks, standards and best practices across the AI and Machine Learning ecosystem.

• Mentor junior engineers and support knowledge sharing within the engineering team.

Core Technical Requirements

Candidates should have strong hands-on experience in:

• Python

• SQL

• Databricks

• Databricks Workflows

• MLflow

• Databricks Model Serving

• Mosaic AI

• Microsoft Azure

• Azure Kubernetes Service (AKS)

• Kubernetes

• Docker

• REST API development

• Microservices

• CI/CD

• MLOps

• Machine Learning

• Generative AI

• Large Language Models (LLMs)

• Retrieval-Augmented Generation (RAG)

• AI Agents

• Spark / distributed computing

• Cloud-native AI/ML platforms

• Monitoring and observability

• Infrastructure automation

Required Experience

The ideal candidate will have:

• Strong experience building, deploying and supporting Machine Learning solutions in production environments.

• Proven experience with MLOps, DevOps and software engineering practices within AI/ML environments.

• Strong development experience using Python, together with SQL and API development.

• Hands-on experience with Databricks, MLflow and Model Serving.

• Strong experience with Kubernetes, Docker and containerised application deployment.

• Experience deploying Machine Learning and/or Generative AI workloads onto Azure Kubernetes Service (AKS).

• Experience implementing CI/CD pipelines, infrastructure automation and production monitoring.

• Experience with Spark or other distributed / large-scale data processing technologies.

• Practical understanding of LLMs, RAG architectures, Generative AI and AI agents.

• Experience taking AI or Machine Learning use cases from development through to production deployment and support.

• Experience working collaboratively with Data Scientists and engineering teams.

• Strong troubleshooting skills across applications, APIs, ML pipelines and cloud platforms.

• Ability to translate technical solutions into measurable business outcomes.

Qualifications

A relevant tertiary qualification in one of the following or a related discipline:

• Computer Science

• Engineering

• Econometrics

• Mathematical Statistics

• Actuarial Science

A Master's or Doctorate would be advantageous.

Preferred Certifications

Relevant certifications would be beneficial, including:

• Microsoft Azure certifications such as AZ-104, AZ-305 or AI-102

• Databricks Data Engineer, Machine Learning Engineer or Generative AI Engineer

• Kubernetes certifications such as CKA or CKAD

• DevOps, MLOps or Platform Engineering certifications

• AWS or Google Cloud certifications

• Recognised Machine Learning, AI or Data Science certifications

Ideal Candidate Profile

This role would suit an experienced Machine Learning Engineer, MLOps Engineer, AI Engineer or GenAI Engineer who combines strong software engineering capabilities with practical Machine Learning and cloud infrastructure experience.

The strongest candidates will have previously built and supported enterprise AI/ML platforms, rather than only developing models in notebook or research environments. They should be comfortable working across the full lifecycle from model development and experimentation through to APIs, containers, Kubernetes deployments, monitoring, governance and ongoing production support.