As an MLOps Engineer, you will play a pivotal role in designing, building, and operating the platforms, pipelines, and infrastructure that enable machine learning solutions to move reliably from experimentation to production. This is a blended role combining MLOps, Data Engineering, and DevOps, well-suited to an engineer who enjoys working across the full ML and data lifecycle.
You will work within dynamic, multi-disciplinary project teams, collaborating closely with data scientists, software engineers, and client stakeholders to deliver scalable, production-ready ML and data solutions across a diverse range of commercial sectors.
This role offers strong opportunities for career development, exposure to real-world ML systems, and continuous learning within a fast-growing engineering team.
Key Responsibilities & Skills:
- Own and manage the end-to-end lifecycle of machine learning models, including development, training, deployment, monitoring, and continuous improvement.
- Productionise ML models built with PyTorch, TensorFlow, or Scikit-learn, ensuring reliability, scalability, and security.
- Implement model versioning and experiment tracking using MLflow, along with controlled releases, rollback strategies, and continuous improvement workflows.
- Design, build, and optimize robust data pipelines in Databricks, feeding ML models and analytics workloads.
- Develop and maintain scalable data architectures including Azure Data Lake, Azure SQL Data Warehouse, and Synapse Analytics.
- Build and maintain reliable ETL/ELT workflows, ensuring high data quality, accessibility, and performance.
- Collaborate with data scientists and analysts to translate business and data requirements into production-grade ML pipelines.
- Develop and maintain CI/CD pipelines for software, data, and ML workflows using tools such as Azure DevOps, Git, and Jenkins.
- Automate model training, testing, validation, and deployment using MLflow and Databricks Jobs.
- Automate infrastructure provisioning, environment management, and deployment using Terraform, ARM templates, or Ansible.
Essential Technical Skills:
- Programming: Python, SQL, PySpark, Bash
- ML Frameworks: TensorFlow, PyTorch, Scikit-learn
- MLOps & Model Management: MLflow, Model versioning, CI/CD
- Cloud & Big Data: Databricks, Databricks APIs, MCP, Blob Storage
- Data Engineering: ETL/ELT pipelines, DLT, Delta Lake
- Infrastructure as Code (IaC): Terraform, ARM templates, Ansible
- Version Control & DevOps: Git, Azure DevOps, Jenkins
Experience
- 3+ years of experience in ML/MLOps engineering or a similar role.
- Proven track record of delivering data solutions in a consulting or client-facing environment is a plus.
- Experience with Agile or Scrum methodologies is beneficial.
- The role will principally centre on MLFlow as an MLOps platform, but similar experience in platforms such as SageMaker or VertexAI is a definite benefit.
The Company
Optima Partners is an advanced data and business consultancy headquartered in Edinburgh, UK. We are a practitioner-led organisation that collaborates with top consumer brands to drive transformation and foster customer-centricity through our expertise in customer strategy, innovative design, and advanced data science and engineering.
We help our clients get closer to their customers, to truly understand them, and deliver exactly the right products, engagement, and experiences across all channels and interactions. We specialise in unlocking latent value within organizations through a three-pronged approach that focuses on identifying and enhancing value for customers, within the customer base, and within the business itself. In doing so, we foster sustainable value, paving the way for consistent business growth.
We work with leading consumer brands to tackle and overcome complex business and customer problems to drive transformation and champion customer-centric agendas. We are proud to include some of the leading UK and global brands among our current clients such as Lloyds Banking
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