Senior MLOps Tech Lead — End-to-End ML Infra & Frontend

Parser; Greater London; Hybrid; GBP 120,000 - 180,000; Full time

Benefits offered by this job

Medical insurance

Hybrid working environment

Highly competitive compensation

Job summary

Parser in London is seeking a Tech Lead with a strong MLOps background to design and deliver ML infrastructure, owning the end-to-end stack from backend to frontend while guiding a 5+ engineer team.

You will collaborate with data scientists and cross‑functional stakeholders to deploy scalable ML systems, migrate from MLflow to AWS SageMaker, and enforce best-practice MLOps across training, serving, monitoring and deployment.

Qualifications

• 10+ years of experience in Software, Data, or ML Engineering roles.
• Proven track record as a Tech Lead, owning end-to-end technical delivery across backend and frontend systems.
• Strong expertise in MLOps (training pipelines, serving, monitoring) and Python proficiency.

Responsibilities

• Own the end-to-end delivery of ML systems from backend to frontend.
• Lead architectural decisions across the ML stack for scalability and reliability.
• Drive migration from MLflow to AWS SageMaker with minimal disruption.
• Define and enforce MLOps best practices across training, serving, monitoring and deployment.
• Design scalable ML infrastructure for batch and real-time environments and robust ETL/ELT pipelines.
• Develop and maintain React-based frontend interfaces to surface ML insights.
• Mentor a team of 5+ engineers, providing structured feedback.
• Collaborate with cross-functional teams to address blockers.

Skills

Tech Lead

Python

MLOps

React

Data pipelines

Cloud architecture

Leadership

Communication

Tools

MLflow

AWS

Docker

Spark

Kafka

SageMaker

Job description

Parser in London is seeking a Tech Lead with a strong MLOps background to design and deliver ML infrastructure, owning the end-to-end stack from backend to frontend while guiding a 5+ engineer team.

You will collaborate with data scientists and cross‑functional stakeholders to deploy scalable ML systems, migrate from MLflow to AWS SageMaker, and enforce best-practice MLOps across training, serving, monitoring and deployment.