Machine Learning Engineer

Vallum Associates; United Kingdom; Remote; GBP 60,000 - 80,000; Full time

Job summary

A technology consulting firm in the UK is seeking an experienced MLOps Data Engineer to design, build, and maintain data pipelines and machine learning infrastructure. This role involves developing robust solutions for ML lifecycle management and requires strong programming skills in Python and SQL, along with proficiency in AWS and data engineering tools. Join a dynamic team to operationalize models and ensure data integrity.

Qualifications

• Strong programming skills in Python and SQL.
• Experience with AWS and data engineering tools.
• Hands-on experience with MLOps frameworks like MLflow and Kubeflow.

Responsibilities

• Develop and maintain data pipelines for ML and analytics.
• Implement MLOps best practices for model deployment.
• Build and optimize ETL/ELT processes for data.

Skills

Python

SQL

AWS

Data engineering tools

MLOps frameworks

Containerization

Data modeling

Tools

Spark

Kafka

Airflow

dbt

MLflow

Kubeflow

Vertex AI

SageMaker

Docker

Kubernetes

Job description

Job description

(Remote)

You will be designing, building and maintaining data pipelines and machine learning infrastructure that support scalable, reliable, and production-ready AI/ML solutions. You will work closely with data scientists, engineers, and product teams to operationalize models, streamline workflows, and ensure data quality and availability.

• Develop and maintain data pipelines to support machine learning and analytics use cases.

• Implement MLOps best practices for model deployment, monitoring, and lifecycle management.

• Build and optimize ETL/ELT processes for structured and unstructured data.

• Automate workflows for training, testing, and deploying ML models.

• Ensure data integrity, governance, and security across the ML lifecycle.
  

MLOps Data Engineer Experience

• Strong programming skills in Python, SQL, and experience with AWS

• Proficiency with data engineering tools (e.g., Spark, Kafka, Airflow, dbt).

• Hands-on experience with MLOps frameworks (e.g., MLflow, Kubeflow, Vertex AI, SageMaker).

• Familiarity with CI/CD pipelines, containerization (Docker, Kubernetes)

• Solid understanding of data modeling, warehousing, and APIs.

• Strong problem-solving skills and ability to work in agile environments.