Senior Software Engineer - MLOps
Senior Software Engineer - MLOps

Senior Software Engineer - MLOps

City of London Full-Time 43200 - 72000 £ / year (est.) No home office possible
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At a Glance

  • Tasks: Join a team to design and build ML infrastructure for a leading quant firm.
  • Company: A top-tier quant finance firm investing heavily in technology and innovation.
  • Benefits: Enjoy competitive salary, generous bonuses, 25 days holiday, and a monthly WFH allowance.
  • Why this job: Work on impactful projects with autonomy and tackle complex challenges in ML workflows.
  • Qualifications: Significant ML Ops experience and strong Python coding skills required.
  • Other info: Opportunity to influence greenfield infrastructure in a collaborative environment.

The predicted salary is between 43200 - 72000 £ per year.

We’re looking for an experienced ML Ops engineer to join a newly formed team in a leading quant firm, responsible for ML Operations across a next-generation research platform. This is a high-impact, greenfield role where you’ll help design and build the future of ML infrastructure—from how data is shared, to how models are trained, deployed, and supported in production. ML is central to the firm’s trading strategies, and the platform you help shape will directly empower researchers and drive real business outcomes. Expect high autonomy and deep technical challenges—off-the-shelf tools won’t cut it, so you’ll often build bespoke solutions to handle complex interdependencies in ML workflows.

The Role: As part of the ML Workflows team, you’ll take ownership of building a mature, scalable ML research and deployment pipeline. You’ll work across the full ML lifecycle, including:

  • Ingesting and managing new datasets
  • Building tools for distributed training and inference
  • Creating robust deployment and production support systems

You’ll leverage your ML Ops experience to assess the current landscape, identify gaps, and lead the technical direction of the new platform. Projects you’ll work on include:

  • Implementing best-practice feature and model stores
  • Proper versioning of features, data, and models
  • Improving inference compute utilisation through smarter serving
  • Building CI/CD pipelines for ML workflows
  • Solving complex job orchestration for model training
  • Developing tooling for robust validation, monitoring, and recovery in production

We’re looking for engineers who thrive on complex, open-ended challenges and want to set new standards for ML infrastructure.

You should have:

  • Significant experience in ML Ops
  • Strong coding skills in Python
  • A deep understanding of ML lifecycle pain points and practical solutions
  • Experience building systems for scaling training, versioning, and deployment
  • Bonus points for experience with distributed compute, data engineering, and orchestration frameworks (e.g. Airflow, Ray, KubeFlow).

Why join? Top-tier quant finance firm with huge tech investment. Competitive base salary + 50–100%+ annual bonus. 25 days holiday, monthly WFH allowance, and £20/day lunch budget. Brand new, world-class offices in central London. Surrounded by some of the sharpest minds in engineering and research. If you’re ready to have real influence, work on greenfield infrastructure, and shape the ML future in a top business—we’d love to hear from you.

Senior Software Engineer - MLOps employer: Vertex Search

Join a leading quant firm in central London as a Senior Software Engineer - MLOps, where you'll be at the forefront of building innovative ML infrastructure that directly impacts trading strategies. Enjoy a competitive salary, generous bonuses, and a supportive work culture that fosters autonomy and collaboration among some of the brightest minds in the industry. With opportunities for professional growth and a focus on cutting-edge technology, this role offers a unique chance to shape the future of machine learning in finance.
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Contact Detail:

Vertex Search Recruiting Team

StudySmarter Expert Advice 🤫

We think this is how you could land Senior Software Engineer - MLOps

✨Tip Number 1

Familiarise yourself with the latest trends and technologies in ML Ops. Being well-versed in tools like Airflow, Ray, and KubeFlow can set you apart, especially since the role involves building bespoke solutions for complex ML workflows.

✨Tip Number 2

Network with professionals in the quant finance and ML Ops space. Attend relevant meetups or webinars to connect with others who might provide insights or even referrals to our open positions.

✨Tip Number 3

Showcase your problem-solving skills by discussing past projects where you tackled complex ML lifecycle challenges. Be prepared to share specific examples during interviews that highlight your experience in building scalable systems.

✨Tip Number 4

Research our company culture and values. Understanding what we stand for will help you align your responses during interviews and demonstrate how you can contribute to our mission of shaping the future of ML infrastructure.

We think you need these skills to ace Senior Software Engineer - MLOps

ML Ops Expertise
Strong Python Coding Skills
Understanding of ML Lifecycle
Experience with Distributed Training
Model Deployment Techniques
Version Control for Features and Models
CI/CD Pipeline Development
Job Orchestration Solutions
Data Management and Ingestion
Monitoring and Recovery Systems
Tooling for Validation in Production
Knowledge of Orchestration Frameworks (e.g. Airflow, Ray, KubeFlow)
Problem-Solving Skills
Ability to Work Autonomously
Adaptability to Complex Challenges

Some tips for your application 🫡

Tailor Your CV: Make sure your CV highlights your experience in ML Ops, particularly focusing on your coding skills in Python and any relevant projects you've worked on. Use specific examples that demonstrate your ability to handle complex ML lifecycle challenges.

Craft a Compelling Cover Letter: In your cover letter, express your enthusiasm for the role and the company. Discuss how your background aligns with their needs, especially your experience with building scalable ML systems and your understanding of the ML lifecycle.

Showcase Relevant Projects: If you have worked on projects involving distributed compute, data engineering, or orchestration frameworks like Airflow or KubeFlow, be sure to include these in your application. Highlight your contributions and the impact they had on the projects.

Prepare for Technical Questions: Anticipate technical questions related to ML Ops and be ready to discuss your problem-solving approaches. Familiarise yourself with best practices in feature and model stores, CI/CD pipelines, and job orchestration to demonstrate your expertise during interviews.

How to prepare for a job interview at Vertex Search

✨Showcase Your ML Ops Experience

Be prepared to discuss your previous experience in ML Ops in detail. Highlight specific projects where you built or improved ML infrastructure, focusing on the challenges you faced and how you overcame them.

✨Demonstrate Technical Proficiency

Since strong coding skills in Python are essential for this role, be ready to demonstrate your coding abilities. You might be asked to solve a problem or write code during the interview, so practice common algorithms and ML workflows beforehand.

✨Understand the ML Lifecycle

Familiarise yourself with the entire ML lifecycle, from data ingestion to model deployment. Be prepared to discuss pain points you've encountered and practical solutions you've implemented in past roles.

✨Prepare for Complex Problem-Solving

Expect to face open-ended challenges during the interview. Think about how you would approach complex job orchestration for model training or improving inference compute utilisation, and be ready to articulate your thought process clearly.

Senior Software Engineer - MLOps
Vertex Search
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