MLOps Engineer

Jobtailor; Greater London; On-site; GBP 70,000 - 110,000; Full time

Job summary

Jobtailor is seeking an experienced ML Platform Engineer to build and maintain ML pipelines for Market Pricing. You will productionise Python models and data workflows, moving them from development to repeatable, tested production processes.

You will improve testing, releases, monitoring and maintenance, while collaborating with cross-functional teams including Pricing and Data/Governance stakeholders. We value strong Python and SQL skills, cloud familiarity (Azure/AWS/GCP), and experience with

Qualifications

• Strong Python skills and experience building data or machine learning workflows.
• Good SQL skills and confidence working with structured datasets.
• Experience building reliable, reusable and maintainable code or pipelines.
• Understanding of machine learning fundamentals, especially supervised learning.
• Experience with Git, code review, testing and technical documentation.
• Exposure to cloud-based data or engineering environments.
• Hands-on experience with Azure, AWS or GCP is nice to have.
• Exposure to Snowflake, Databricks, Spark or similar is nice to have.
• Experience with MLOps practices such as model deployment, monitoring, versioning or model registries is nice to have.
• Exposure to Airflow, MLflow, Azure DevOps, GitHub Actions or similar is nice to have
• Experience in pricing, insurance, financial services or another regulated environment is nice to have
• Understanding of model governance, responsible AI or explainability is nice to have

Responsibilities

• Build, improve and maintain machine learning pipelines used within Market Pricing
• Productionise Python-based models, notebooks and data science workflows
• Move models from development into repeatable, tested and documented production processes
• Improve testing, release, monitoring and maintenance of model workflows
• Investigate issues across data, model and pipeline workflows
• Automate manual steps to improve quality, speed or control
• Support Git, code review, testing and documentation practices
• Ensure pipelines and model workflows are traceable, auditable and safe to change
• Collaborate with Pricing, Data Science, Data Engineering, ML Engineering and governance stakeholders
• Own defined MLOps components and pipelines while contributing to standards, architecture and governance

Skills

Python

SQL Proficiency

Git and Code Review

Cloud-Based Environments

MLOps Practices

Machine Learning Workflows

Data Pipeline Development

Model Deployment

Testing and Documentation

Automated Workflows

Problem-Solving

Clear Communication

Tools

Azure

AWS

GCP

Snowflake

Databricks

Spark

Airflow

MLflow

Azure DevOps

GitHub Actions

Job description

• Build, improve and maintain machine learning pipelines used within Market Pricing
• Productionise Python-based models, notebooks and data science workflows
• Move models from development into repeatable, tested and documented production processes
• Improve testing, release, monitoring and maintenance of model workflows
• Investigate issues across data, model and pipeline workflows
• Automate manual steps to improve quality, speed or control
• Support Git, code review, testing and documentation practices
• Ensure pipelines and model workflows are traceable, auditable and safe to change
• Collaborate with Pricing, Data Science, Data Engineering, ML Engineering and governance stakeholders
• Own defined MLOps components and pipelines while contributing to standards, architecture and governance

Requirements

• Strong Python skills and experience building data or machine learning workflows
• Good SQL skills and confidence working with structured datasets
• Experience building reliable, reusable and maintainable code or pipelines
• Understanding of machine learning fundamentals, especially supervised learning
• Experience with Git, code review, testing and technical documentation
• Exposure to cloud-based data or engineering environments
• Good problem-solving skills and attention to detail
• Clear communication skills and ability to explain technical ideas to different audiences
• Comfortable working with data scientists, analysts, engineers and business stakeholders
• Practical focus on quality, reliability, governance and maintainability
• Hands‑on experience with Azure, AWS or GCP is nice to have
• Exposure to Snowflake, Databricks, Spark or similar is nice to have
• Experience with MLOps practices such as model deployment, monitoring, versioning or model registries is nice to have
• Exposure to Airflow, MLflow, Azure DevOps, GitHub Actions or similar is nice to have
• Experience in pricing, insurance, financial services or another regulated environment is nice to have
• Understanding of model governance, responsible AI or explainability is nice to have
• Must be able to complete the employer's thorough referencing process, including credit and criminal record checks
• Employer is unable to offer sponsorship for this position

Core Competencies

Demonstrates strong capabilities in building and maintaining machine learning pipelines, with a focus on Python programming, SQL proficiency, and MLOps practices. Emphasizes collaboration with cross-functional teams and a commitment to quality, reliability, and governance in data workflows.

Highest-signal resume keywords

• Python Programming
• SQL Proficiency
• MLOps Practices
• Git and Code Review
• Cloud-Based Environments

ATS Optimization Keywords

Hard Skills

• Machine Learning Workflows
• Data Pipeline Development
• Model Deployment
• Testing and Documentation
• Automated Workflows

Soft Skills

• Problem-Solving
• Attention to Detail
• Clear Communication

Industry Keywords

• Pricing
• Insurance
• Financial Services
• Model Governance
• Responsible AI

Tools & Technologies

• Azure
• AWS
• GCP
• Snowflake
• Databricks
• Spark
• Airflow
• MLflow
• Azure DevOps
• GitHub Actions