Senior Machine Learning Engineer - AI​/GenAI

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
.

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

• Product ionise 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.

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

The ideal candidate will have:

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

• Proven experience with
  MLOps, Dev Ops 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,…