EM MlOps

dunnhumby is the global leader in Customer Data Science, empowering businesses everywhere to compete and thrive in the modern data-driven economy. We always put the Customer First.

Dunnhumby(a Tesco company) Hq. in London with offices across countries employs nearly 2,500 experts in offices throughout Europe, Asia, Africa, and the Americas working for transformative, iconic brands such as Tesco, Coca-Cola, Meijer, Procter & Gamble and Metro.

Dunnhumby helps retailers and brands deliver better experiences through Customer First strategies.

Our mission: to enable businesses to grow and reimagine themselves by becoming advocates and champions for their Customers. With deep heritage and expertise in retail one of the world's most competitive markets, with a deluge of multi-dimensional data dunnhumby today enables businesses all over the world, across industries, to be Customer First.

What is a Retail Media Network (RMN)

A Retail Media Network is an advertising platform owned by a retail company that allows brands to advertise to the retailer's customers on the retailer's own properties and sometimes beyond.

In practice, this means retailers like Amazon, Walmart, Target, etc., offer ad placements on their websites, apps, in-store displays, or other channels, leveraging their first-party shopper data.

We are seeking a talented Engineering ManagerML Ops expertise to lead a team of engineers in developing product that help Retailers transform their Retail Media business in a way that helps them achieve maximum ad revenue and enable massive scale.

As an Engineering Manager, you will play a pivotal role in designing and delivering high-quality software solutions.

You will be responsible for leading a team, mentoring engineers, contributing to system architecture, and ensuring adherence to engineering best practices. Your technical expertise, leadership skills, and ability to drive results will be key to the success of our products.

What you will be doing

You will lead the charge in ensuring operational efficiency and delivering high-value solutions. You'll mentor and develop a high-performing team of Big Data and MLOps engineers, driving best practices in software development, data management, and model deployment. With a focus on robust technical design, you'll ensure solutions are secure, scalable, and efficient.

Your role will involve hands-on development to tackle complex challenges, collaborating across teams to define requirements, and delivering innovative solutions. You'll keep stakeholders and senior management informed on progress, risks, and opportunities while staying ahead of advancements in AI/ML technologies and driving their application. With an agile mindset, you will overcome challenges and deliver impactful solutions that make a difference.

Technical Expertise:

• Proven experience in microservices architecture, with hands-on knowledge of Docker and Kubernetes for orchestration.
• Proficiency in ML Ops and Machine Learning workflows using tools like Spark.
• Strong command of SQL and PySpark programming.
• Expertise in Big Data solutions such as Spark and Hive, with advanced Spark optimizations and tuning skills.
• Hands-on experience with Big Data orchestrators like Airflow.
• Proficiency in Python programming, particularly with frameworks like FastAPI or equivalent API development tools.
• Experience in data engineering is added advantage
• Experience in unit testing, code quality assurance, and the use of Git or other version control systems.

Cloud and Infrastructure:

• Practical knowledge of cloud-based data stores, such as Redshift and BigQuery (preferred).
• Experience in cloud solution architecture, especially with GCP and Azure.
• Familiarity with GitLab CI/CD pipelines is a bonus.

Monitoring and Scalability:

• Solid understanding of logging, monitoring, and alerting systems for production-level big data pipelines.
• Prior experience with scalable architectures and distributed processing frameworks.

Soft Skills and Additional Plus Points:

• A collaborative approach to working within cross-functional teams.
• Ability to troubleshoot complex systems and provide innovative solutions.
• Familiarity with GitLab for CI/CD and infrastructure automation tools is an added advantage.