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Lead MLOps Engineer

We’re on a mission to provide equitable access to economic opportunity, for everyone.

We close critical skill gaps in the workforce through a new kind of apprenticeship that combines work and learning. We begin by recognizing high-potential individuals both inside and outside of a company's current workforce and then we create applied, guided and equitable learning programs, with measurable impact. Because we believe the world needs a better way to match its potential.

We work with over 1,500 leading companies including the likes of Microsoft, Citi and Just Eat to help solve their business-critical problems, and we’ve trained over 16,000 professional apprentices in the tech and data skills of the future. This is made possible by our global team who are driven to achieve a mission that matters, together.

Join Multiverse and help us set a new course for work.



The Opportunity

As a Lead Machine Learning Operations Engineer (MLOps), you will be responsible for developing and maintaining Multiverse’s Machine Learning pipeline - establishing, automating, optimising and securing data flows from, through and into multiple data sources that our AI Team will use to serve all parts of the business.

This role will be within the AI Team, working closely with other ML Engineers, Data Scientists, ML Ops Engineers, Product Managers. While your primary focus will be on MLOps, you will also be asked to support the team with its broader Infrastructure & DevOps needs. The AI Team’s mission is to build the intelligence layer at Multiverse that fuels captivating educational experiences, drives effective and continuous digital transformation for enterprises, and empowers Multiverse coaches and operations with tools that scale our teaching capabilities.

You will need to be methodical, analytical, creative and tenacious with very high attention to detail. And while this is a technical role, our team culture is one where everyone is expected to collaborate well and take ownership of both deliverable & stakeholder management.

What you’ll focus on:

  • Driving strategic decisions around MLOps tooling, frameworks, and infrastructure as the AI teams need evolve

  • Designing, automating, developing and maintaining our infrastructure, pipelines, and development processes that enable greater operational efficiency

  • Deploying, managing, and optimising AI solutions in production environments, ensuring smooth integration and efficient operations

  • Experiment with data science and machine learning techniques to adapt AI solutions for production applications

  • Creating systems for deployment, CI/CD, testing, and monitoring

  • Identify and address system integrity and security risks

What we’re looking for:

  • A proven track record of delivering complex ML infrastructure projects in production

  • Deep Experience with Azure, e.g. API management, ML stack

  • Experience coding at production level with Python

  • Strong foundations in software engineering, building ML models, and DevOPs

  • Experience with multiple cloud environments, PostgreSQL, GitHub, CircleCI/Jenkins (or similar), CI/CD and Infrastructure as Code (e.g. Terraform)

  • Experience with model experiment tracking

  • Commitment to Multiverse’s mission and values

You will most likely thrive and enjoy this role if:

  • You have a meticulous attention to detail

  • You are an exceptional communicator (especially cross-functionally), with an ability to translate technical concepts to the appropriate level of detail to the right audience

  • Take a tenacious, curious, and pragmatic approach to problem solving, with a focus on generating usable and scalable outputs

  • Enjoy having a lot of ownership to build services, processes, and infrastructure from scratch

  • Pride yourself in having a growth mindset, being coachable, and learning every day

How we hire

Benefits

  • Time off - 27 days holiday, plus 7 additional days off: 1 life event day, 2 volunteer days and 4 company-wide wellbeing days and 8 bank holidays per year

  • Health & Wellness- private medical Insurance with Bupa, a medical cashback scheme, life insurance, gym membership & wellness resources through Gympass and access to Spill - all in one mental health support

  • Hybrid & remote work offering - with weekly or monthly visits to the London office and the opportunity to work abroad 45 days a year

  • Team fun - weekly socials, company wide events and office snacks!

Our commitment to Diversity, Equity and Inclusion

We’re an equal opportunities employer. And proud of it. Every applicant and employee is afforded the same opportunities regardless of race, colour, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender, gender identity or expression, or veteran status. This will never change. Read our Equality, Diversity & Inclusion policy here.

Right to Work

Do you have the right to work in the UK? Unfortunately, at this time we cannot offer sponsorship for this role and we cannot consider overseas applications.

Safeguarding

All posts in Multiverse involve some degree of responsibility for safeguarding. Successful applicants are required to complete a Disclosure Form from the Disclosure and Barring Service ("DBS") for the position. Failure to declare any convictions (that are not subject to DBS filtering) may disqualify a candidate for appointment or result in summary dismissal if the discrepancy comes to light subsequently. 

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CEO of Multiverse
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Average salary estimate

$105000 / YEARLY (est.)
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$120000K

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What You Should Know About Lead MLOps Engineer, Multiverse

At Multiverse, we’re on a mission to bridge the gap in economic opportunity for everyone, and we're looking for a passionate Lead MLOps Engineer to help us achieve this. Based in London, this role is perfect for someone who thrives in a dynamic environment and is eager to contribute to impactful projects. You'll be at the heart of developing and maintaining our Machine Learning pipeline, ensuring our AI Team has the tools they need to deliver exceptional educational experiences. As a Lead MLOps Engineer, you'll collaborate with talented ML Engineers, Data Scientists, and Product Managers to automate and optimize data flows that drive our operations. Your work will not only support internal teams but also help over 16,000 apprentices gain essential tech and data skills. You’ll need a strong background in Azure, Python, and production-level ML infrastructure to tackle the complex challenges we face. We celebrate creativity, analytical thinking, and a detail-oriented approach in our team, valuing your contributions as we navigate our continuous digital transformation. If you’re ready to take ownership of exciting projects and help us shape the future of learning, we want to hear from you!

Frequently Asked Questions (FAQs) for Lead MLOps Engineer Role at Multiverse
What responsibilities does the Lead MLOps Engineer have at Multiverse?

As the Lead MLOps Engineer at Multiverse, your primary responsibilities will include developing and maintaining the Machine Learning pipeline, automating and optimizing data flows, and ensuring the integration of AI solutions into production environments. You'll collaborate closely with the AI Team, focusing on refining infrastructure, deploying algorithms, and reducing operational risks while enhancing system integrity.

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What qualifications are required for the Lead MLOps Engineer position at Multiverse?

To apply for the Lead MLOps Engineer role at Multiverse, candidates should have a proven track record in delivering complex ML infrastructure projects. Essential qualifications include experience with Azure, Python coding at a production level, and familiarity with tools like GitHub, CircleCI or Jenkins, and Infrastructure as Code (e.g., Terraform). A background in DevOps practices and model experiment tracking is highly desirable.

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What skills are important for success as a Lead MLOps Engineer at Multiverse?

Success as a Lead MLOps Engineer at Multiverse hinges on a few key skills, including meticulous attention to detail, exceptional communication abilities, and a strong problem-solving mindset. You should be comfortable collaborating with cross-functional teams and have the adaptability to translate complex technical concepts into understandable terms for different audiences.

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How does Multiverse support the professional growth of its Lead MLOps Engineer?

At Multiverse, we believe in continual growth and learning. As a Lead MLOps Engineer, you will have opportunities to build new services and infrastructure from scratch, require a growth mindset, and work collaboratively with other experts in the AI team. We also provide resources for learning and development, helping you enhance your skill set while contributing meaningfully to our mission.

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What does the team culture look like for the Lead MLOps Engineer at Multiverse?

The team culture at Multiverse is collaborative and supportive. As a Lead MLOps Engineer, you'll be expected to take ownership of deliverables while working closely with a diverse group of professionals. We value communication, teamwork, and a willingness to share knowledge and ideas, fostering an environment where innovation can thrive.

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Common Interview Questions for Lead MLOps Engineer
Can you describe your experience with Azure as a Lead MLOps Engineer?

When answering this question, focus on specific projects where you've utilized Azure for ML solutions. Discuss how you managed API integration, worked with the ML stack, and any challenges you faced. Be prepared to showcase your problem-solving strategies and how you optimized cloud resources to enhance project outcomes.

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What types of machine learning projects have you deployed in production environments?

Discuss the types of projects you've worked on, such as recommendation systems, predictive analytics, or natural language processing applications. Mention your role, the technologies used, and the outcomes. Emphasize your ability to navigate production challenges and ensure model performance post-deployment.

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How do you manage CI/CD pipelines in ML operations?

In your response, explain the CI/CD processes you’ve implemented for automated testing, integration, and deployment of ML models. Highlight tools used such as GitHub Actions or Jenkins, and share insights on how these practices improved your projects' efficiency and reliability, ultimately leading to quicker iterations.

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What strategies do you use for data flow automation in production?

Outline your strategies for automating data flows, including the tools and methods used. Discuss how you implemented data ingestion, transformation, and storage processes that allow seamless access for machine learning applications. Highlight any specific frameworks or pipelines that have been particularly successful for you.

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How do you ensure the security and integrity of ML systems?

Explain your approach to identifying security risks and implementing measures to mitigate them. Focus on your experiences with data encryption, access controls, and ongoing monitoring. Discuss any frameworks or practices you follow to enhance system integrity in your ML pipelines.

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Can you discuss a challenging problem you encountered in MLOps and how you resolved it?

Share a specific example of a significant challenge related to MLOps, detailing the situation, your analysis of the issue, and the strategies you employed to address it. Emphasize the skills and knowledge you drew upon to find a successful resolution.

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How do you keep up with the latest trends and technologies in MLOps?

Discuss the resources you utilize to remain updated, such as attending conferences, participating in online forums, or following industry leaders. Share specific examples of how you have applied new knowledge or tools to improve your work or team’s output.

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What is your approach to collaborating with cross-functional teams?

Outline your strategies for effective communication and collaboration. Highlight how you engage with members from different technical backgrounds and translate complex information to ensure understanding. Share instances where cross-functional teamwork led to successful project outcomes.

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What tools do you typically use for model experiment tracking?

Discuss the experiment tracking tools you’ve used, such as MLflow, DVC, or Weights & Biases. Highlight how these tools contribute to reproducibility in machine learning, aid in model versioning, and improve collaboration among team members.

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How do you handle performance monitoring for deployed ML models?

Explain your approach to monitoring model performance after deployment, including metrics you track and how you address any degradation issues. Touch on the use of dashboards, alerts, and retraining strategies to ensure the models remain effective over time.

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Our mission is to build an outstanding alternative to university and corporate training, creating a diverse group of future leaders. To achieve this, we provide high-quality apprenticeship programmes that combine work, training and community.

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Full-time, hybrid
DATE POSTED
January 14, 2025

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