MLOps Engineer - Forecasting / Cloud

Xcede Recruitment Solutions; City Of London; Remote; GBP 60,000 - 80,000; Full time

Job summary

A leading technology recruitment agency is seeking an experienced MLOps Engineer to design and implement ML-based forecasting models within cloud environments. You will be responsible for building ML pipelines and providing technical guidance across teams. Ideal candidates will have a strong Python background and experience in energy markets. This role is remote-friendly within the UK.

Qualifications

• 5+ years in software/ML engineering with production deployment experience.
• Strong Python background with data frameworks.
• Proven experience with ML pipelines and cloud integration.

Responsibilities

• Design and implement forecasting models and ML-based algorithms.
• Build and maintain ML pipelines and CI/CD processes.
• Optimize performance using AWS and Databricks.

Skills

Python

ML pipelines

AWS

Databricks

CI/CD processes

Collaboration

Tools

Pandas

NumPy

SciPy

Terraform

CloudFormation

Job description

MLOps Engineer - Forecasting / Cloud

Remote - UK (O/IR35), NL, BE, GER

3 - 6 Months initial contact

Join an innovative technology company modernising its data science and AI capabilities. You’ll take ownership of how machine learning models are built, deployed, and scaled across distributed cloud environments — helping the business embed modern AI best practices and robust MLOps pipelines.

The Role

You’ll design, implement, and productionise forecasting models and ML-based algorithms that improve decision‑making across the energy domain. This includes defining and setting up the end‑to‑end ML infrastructure, mentoring engineers, and shaping how the organisation approaches MLOps and AI enablement.

What You’ll Do

• Build and maintain ML pipelines and CI/CD processes for model training, validation, and deployment.
• Lead the implementation of forecasting models and supervised learning approaches within scalable cloud environments.
• Work with engineering and product teams to embed ML capabilities into production systems.
• Optimise performance using tools such as AWS, Databricks, and containerisation frameworks.
• Define best practices for MLOps, monitoring, and version control.
• Provide technical guidance and education to teams adopting AI tooling.

What You’ll Bring

• 5+ years in software/ML engineering, ideally with production deployment experience.
• Strong Python background, comfortable across data frameworks like Pandas, NumPy, SciPy, Dask, Polars, or PySpark.
• Proven experience setting up ML pipelines, integrating with AWS / Databricks, and applying CI/CD principles.
• Solid understanding of time‑series forecasting and supervised ML models.
• Knowledge of cloud infrastructure, IaC (Terraform / CloudFormation), and containerisation.
• Excellent collaboration and communication skills — confident discussing architecture with data scientists, engineers, and business leads.
• Experience in energy markets is a significant advantage (especially UK or European retail)