MLOps Specialist

4 weeks ago

London, Greater London, United Kingdom Apron Full time

At Apron, we were founded by a team of professionals who have dedicated years to developing products for leading fintech enterprises. However, we identified a significant challenge that remained unaddressed: business payments. These are the transactions that facilitate the purchase of essential items, from produce to tools, ensuring that suppliers are satisfied and businesses thrive. Ideally, these payment processes should be straightforward to execute and manage, yet they often prove to be complex and time-consuming, consuming valuable hours for both businesses and their financial advisors.

This issue impacts a diverse range of entrepreneurs, from florists to financial analysts, brewers to brand strategists. Imagine the possibilities if they could reclaim that time. What innovations could they bring to life? What new ideas could they explore?

That’s the motivation behind Apron – to transform the payment experience from a hindrance to a catalyst for growth. We consolidate all payment-related activities, seamlessly integrating them into your workflow, streamlining communications, and reducing hours of work into mere minutes. This allows you to redirect your time towards more productive endeavors – whether that’s planning for the future, enjoying a walk, or connecting with loved ones.

We are proud to be supported by Index Ventures and Bessemer Venture Partners.

Who We Are Seeking

We are in the process of developing a product that enables our clients to upload invoices and receipts, which will then be automatically processed and prepared for payment. Our ambition is to establish this product as the leading solution in the market.

To realize this vision, we require the creation of a document recognition service that delivers outstanding quality, speed, and reliability.

We are looking for an engineer to assist in constructing the infrastructure necessary for training and deploying models for this service.

Another crucial aspect of this role involves ensuring that the relevant data for model training, inference, and analytics is readily available. This will necessitate skills in building data pipelines, conducting data quality assessments, and managing databases and infrastructure.

The ideal candidate will possess extensive experience and a comprehensive understanding of MLOps and Data Engineering. They should be adept at determining which technologies will be advantageous and which may be superfluous, ensuring that our solutions remain efficient and straightforward.

Your Responsibilities
  • Develop and manage data pipelines, optimizing data infrastructure to guarantee the availability of relevant data for model training, inference, and analytics.
  • Oversee model serving to ensure high performance under heavy loads, and establish monitoring dashboards and alerts.
  • Recommend suitable architecture and tools for model serving, including the use of GPUs when necessary.
  • Implement MLOps tools for model development and serving, including dataset and model storage and versioning, reproducible model training, and metrics visualization.
  • Set up document labeling tools and facilitate model retraining based on real-time feedback.
  • Ensure data security throughout service and training pipelines.
  • Contribute to the establishment of development best practices, including testing within the team.

Qualifications

  • 5+ years of experience in MLOps, machine learning, and data engineering.
  • Proficient in Python and SQL (PostgreSQL preferred).
  • Experience in developing data pipelines and managing data (e.g., using Airflow or similar tools).
  • Familiarity with serving machine learning models and utilizing MLOps tools (e.g., MLflow, DVC, or similar).
  • Experience with cloud computing platforms (we utilize GCP) and containerization technologies (e.g., Docker, Kubernetes).
  • Knowledge of Kotlin is advantageous, as all backend code, except for ML services, is written in this language.
  • Experience with A/B testing platforms is a plus.
  • Experience in building infrastructure for online metrics monitoring is a plus (e.g., Kafka, Grafana).
  • Basic understanding of machine learning algorithms, models, and statistical concepts.

What We Offer

  • Competitive salary and stock options.
  • Fully funded technology resources.
  • Health insurance through AXA.
  • Flexible vacation and remote work options.

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