Senior Machine Learning Engineer - AI / GenAI

Duties & Responsibilities

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.

Desired Experience & Qualification

Senior Machine Learning Engineer, ML Engineer, Machine Learning Engineer, AI Engineer, GenAI Engineer, Generative AI Engineer, MLOps Engineer, AI Platform Engineer, Databricks, Databricks Workflows,