MLOps Tech Lead: End-to-End ML Systems | Remote

Parser Limited; Greater London; Hybrid; GBP 120,000 - 150,000; Full time

Benefits offered by this job

Medical insurance

Hybrid work model

Competitive compensation package

Job summary

Parser Limited is seeking an experienced MLOps Technical Manager to lead the design, architecture, and delivery of ML infrastructure across backend and frontend, while guiding a 5+ engineering team.

The role focuses on deploying scalable ML systems, migrating MLflow to AWS SageMaker, and enforcing MLOps best practices, with collaboration across data science, engineering, and operations in a hybrid London or remote Spain setup.

Qualifications

• 10+ years of experience in Software, Data, or ML Engineering roles.
• Proven track record as a Tech Lead (5+ people), owning end-to-end delivery.
• Deep expertise in MLOps (training pipelines, serving, monitoring, CI/CD) and expert-level Python.
• Strong hands-on experience with MLflow (mandatory) and solid experience with AWS and cloud-native architectures.

Responsibilities

• Own end-to-end technical delivery of ML systems, from backend to frontend.
• Lead architectural decisions across the ML stack, ensuring scalability and reliability.
• Drive migration from MLflow to AWS SageMaker, maintaining continuity and minimizing disruption.
• Define and enforce MLOps practices across model training, serving, monitoring, and deployment.
• Design and maintain scalable ML infrastructure for batch and real-time environments, with ETL/ELT pipelines.
• Develop and maintain React-based frontend interfaces that surface ML insights to stakeholders.
• Lead, mentor, and provide structured feedback to a team of 5+ engineers.
• Collaborate with cross-functional stakeholders across engineering, data science, and operations.

Skills

End-to-end delivery

MLOps

Python

MLflow

AWS

React

ETL/ELT

Docker

Spark

Kafka

Leadership

Tools

MLflow

AWS

Docker

Spark

Kafka

React

Job description

Parser Limited is seeking an experienced MLOps Technical Manager to lead the design, architecture, and delivery of ML infrastructure across backend and frontend, while guiding a 5+ engineering team.

The role focuses on deploying scalable ML systems, migrating MLflow to AWS SageMaker, and enforcing MLOps best practices, with collaboration across data science, engineering, and operations in a hybrid London or remote Spain setup.