Data, AI & Analytics
MLOps Engineer Jobs & Careers
Looking for your next MLOps Engineer opportunity?
HOK Consulting connects MLOps Engineers with organisations building reliable platforms and processes for deploying, monitoring and scaling machine learning and AI models across permanent, contract and interim opportunities.
Whether your experience covers Python, MLflow, Databricks, Azure Machine Learning, AWS SageMaker, Kubernetes, Terraform or CI/CD, explore current opportunities or upload your CV to join HOK’s technology talent network.
Current MLOps Engineer Jobs
Explore current MLOps Engineer opportunities available through HOK Consulting.
Can’t see the right MLOps Engineer role?
If there are currently no suitable MLOps Engineer vacancies listed, upload your CV and tell us what you’re looking for. Our team can contact you when a relevant permanent, contract or interim opportunity becomes available.
What Does an MLOps Engineer Do?
MLOps Engineers build and maintain the platforms, automation and processes used to move machine learning and AI models from development into reliable production environments.
The role brings together Machine Learning, Data Engineering, Cloud and DevOps practices, helping Data Scientists and Machine Learning Engineers deploy models consistently while improving scalability, monitoring, security and operational reliability.
MLOps Engineers commonly work alongside Machine Learning Engineers, AI Engineers, Data Engineers, Data Platform Engineers and cloud teams to automate model deployment, manage infrastructure and monitor the performance of production AI systems.
MLOps Engineer Skills & Experience
The exact technical requirements vary between organisations and AI platforms, but MLOps Engineer roles commonly involve experience across areas such as:
- MLOps
- Machine Learning
- Artificial Intelligence
- Python
- MLflow
- Databricks
- Azure Machine Learning
- Amazon SageMaker
- Google Vertex AI
- Docker
- Kubernetes
- Terraform
- Infrastructure as Code
- CI/CD
- Model Deployment
- Model Monitoring
- Model Serving
- Feature Stores
- Cloud Platforms
- Git
- Monitoring and Observability
- Data Engineering
- Apache Airflow
- Generative AI
MLOps Engineers may specialise in a particular cloud or machine learning platform, while others work across multi-cloud environments and enterprise AI platforms.
Strong automation, software engineering and troubleshooting skills are particularly important because MLOps Engineers are responsible for making machine learning systems repeatable, observable and dependable in production.
Typical MLOps Engineer Responsibilities
- Building automated pipelines for machine learning model deployment
- Developing and maintaining MLOps platforms and infrastructure
- Implementing CI/CD for machine learning and AI workloads
- Deploying models into scalable production environments
- Managing containerised workloads using Docker and Kubernetes
- Provisioning cloud infrastructure using Terraform and infrastructure as code
- Monitoring model performance, availability and operational health
- Implementing logging, alerting and observability for production AI systems
- Supporting model versioning, testing and release management
- Working with Data Scientists and Machine Learning Engineers to operationalise models
- Managing cloud-based machine learning services and environments
- Improving the security, reliability and scalability of machine learning platforms
Responsibilities vary depending on the organisation and whether the role focuses on traditional machine learning, Generative AI, cloud AI platforms or wider enterprise model deployment and governance.
MLOps Engineer Salary Expectations
MLOps Engineer salaries vary according to experience, location, cloud platform, machine learning expertise and the complexity of the production AI environment.
Permanent MLOps Engineer
Around £82,500 per year
UK MLOps market median
Contract MLOps Engineer
Around £575 per day
UK MLOps contract market median
Senior MLOps Engineers and professionals with specialist Generative AI, cloud architecture, Kubernetes, Databricks or large-scale production machine learning experience may command higher salaries or contract rates.
Salary information is provided as a general UK market guide. Current market figures cover vacancies requiring MLOps expertise rather than only vacancies using the exact MLOps Engineer job title.
MLOps Engineer Career Progression
MLOps Engineering can lead into senior machine learning infrastructure, AI architecture and wider data and AI platform leadership positions.
Typical MLOps Engineer Career Path
Alternative Career Paths
MLOps Engineers may also progress into roles such as:
- Machine Learning Engineer
- AI Engineer
- Data Platform Engineer
- AI Architect
- Data Architect
- Platform Architect
The right progression depends on whether you want to deepen your machine learning infrastructure expertise, move into AI architecture or take responsibility for wider data and AI platforms and engineering teams.
Your Next Move
Looking for Your Next MLOps Engineer Opportunity?
If you’re considering your next permanent position, contract assignment or interim opportunity, send HOK Consulting your CV.
Tell us about the machine learning platforms you’ve built or supported, the cloud and MLOps technologies you specialise in and the type of opportunity you’re looking for.
Specialist Recruitment
Data, AI & Analytics Recruitment
MLOps Engineering forms part of HOK Consulting’s wider Data, AI & Analytics recruitment expertise, connecting organisations with professionals across data engineering, data science, artificial intelligence, machine learning, analytics, architecture and data leadership.
Hiring?
Recruiting MLOps Engineers?
HOK Consulting supports organisations looking for experienced MLOps Engineers across permanent, contract and interim requirements.
Whether you’re operationalising machine learning models, building an enterprise AI platform, scaling Generative AI workloads or improving model deployment and monitoring, speak to HOK about your requirements.