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

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

Full-Time 80000 - 100000 £ / year (est.) No working from home possible
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At a Glance

  • Tasks: Lead a team to design and deliver cutting-edge ML infrastructure from backend to frontend.
  • Company: Parser, a forward-thinking tech company based in London.
  • Benefits: Competitive salary, flexible working options, and opportunities for professional growth.
  • Other info: Collaborate with data scientists and cross-functional teams in an innovative environment.
  • Why this job: Join a dynamic team and shape the future of machine learning technology.
  • Qualifications: Strong MLOps experience and leadership skills required.

The predicted salary is between 80000 - 100000 £ per year.

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.

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

Parser is an exceptional employer that champions a culture of innovation and collaboration, offering Senior Java Engineers the chance to work on impactful projects in a fast-growing, multicultural environment. With a hybrid working model based in the UK, employees benefit from competitive compensation, medical insurance, and opportunities for continuous learning alongside top-tier specialists. Join us to take ownership of your work and contribute to meaningful digital transformations while enjoying a flexible work-life balance.

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Contact Details:

Parser Recruitment Team

We think you need these skills to ace Senior MLOps Tech Lead — End-to-End ML Infra & Frontend

MLOps
ML Infrastructure Design
AWS SageMaker
MLflow Migration
Team Leadership
Collaboration with Data Scientists
Scalable ML Systems Deployment