Machine Learning Operations (MLOps) Engineer - VOIS

## Who we are

VOIS (Vodafone Intelligent Solutions) is a strategic arm of Vodafone Group Plc, creating value for customers by delivering intelligent solutions through Talent, Technology & Transformation.

As the largest shared services organisation in the global telco industry with 30,000 FTE, our portfolio of next-generation solutions and services are designed in partnership with customers across Vodafone Group, local markets, and partner markets to simplify and drive growth. With our strategic partner Accenture, we work alongside our Vodafone customers, other Telco and tech companies to drive transformation, meet the challenges of our industry and ensure we stay relevant and resilient. This partnership is a unique, industry-first model which brings together the best of in-house and 3rd party capability.

We work with customers across 28 countries from 10 VOIS locations: Albania, Egypt, Hungary, India, Romania, Spain, Turkey, UK, Germany, Ireland, and with a network of teams in Czech Republic, Italy, Greece, and Portugal.

#VOIS #BeUnrivalled #CreateTheFuture

## About this Role

We are seeking a Machine Learning Operations (MLOps) Engineer to bridge the gap between data science and enterprise-grade production engineering by designing, building, and managing end-to-end MLOps infrastructure across big data and AI ecosystems. This role focuses on transforming experimental machine learning models into reliable, scalable, secure, and continuously operating business services. The successful candidate will work across data engineering, software development, platform engineering, and DevOps disciplines to enable production-ready AI and machine learning solutions that deliver measurable business value.

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## What you’ll do

• Deploy, operationalise, and maintain machine learning models in production environments, ensuring availability, scalability, reliability, and performance.
• Build, optimise, and maintain feature engineering pipelines and reusable data assets that support model development and inference workloads.
• Design, implement, and manage end-to-end MLOps pipelines covering model training, validation, deployment, monitoring, and CI/CD practices.
• Refactor machine learning code into secure, production-ready, maintainable, and reusable services aligned with software engineering standards.
• Monitor model performance, data quality, model drift, system health, and resource utilisation, implementing continuous improvements where required.
• Collaborate with Data Scientists, Software Engineers, Platform Engineers, and business stakeholders to integrate AI and machine learning capabilities into enterprise applications.
• Ensure AI and machine learning solutions comply with enterprise architecture, security, governance, scalability, and operational requirements.
• Drive innovation through automation, reusable engineering patterns, emerging MLOps practices, and modern AI technologies.
• Support production machine learning systems with a focus on deployment, monitoring, optimisation, and operational excellence.
• Contribute to AI platform engineering and the delivery of scalable, integrated AI capabilities across the organisation.

## Who you are

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related discipline.
• Minimum 3 years of experience in MLOps, Machine Learning Engineering, Data Engineering, or a related software engineering role supporting production environments.
• Strong proficiency in Python and SQL, with working knowledge of Java, Scala, and Apache Spark.
• Experience with GitLab, Jenkins, Airflow, MLflow, Hadoop, and cloud-based machine learning platforms.
• Familiarity with AI/ML frameworks used for model development, experiment tracking, deployment, and serving.
• Knowledge of Large Language Models (LLMs), including operationalisation, deployment, monitoring, fine-tuning pipelines, or application integration.
• Experience developing and supporting production machine learning systems, including feature engineering, deployment, monitoring, and iterative enhancement.
• Understanding of data science concepts, experimentation approaches, and model evaluation metrics.
• Ability to convert existing models and pipelines into production-grade solutions and support their ongoing operation.
• Strong coding, debugging, and problem-solving capabilities.
• Understanding of distributed systems, big data technologies, cloud computing principles, and physical server administration.
• Experience with cloud and container technologies such as AWS, Google Cloud Platform, Microsoft Azure, Docker, and Kubernetes.
• Advanced knowledge of SQL and NoSQL technologies including Oracle, PostgreSQL, Hive, HBase, Cassandra, HDFS, AWS S3, and related data platforms.
• Experience with Apache Hadoop and large-scale data processing environments.
• Working knowledge of system administration practices.
• Ability to collaborate effectively with cross-functional technical and business teams.

## Not a perfect fit?

Concerned you may not meet every requirement? Vodafone is committed to creating an inclusive workplace where everyone can thrive. If you are excited about this role but your experience does not align exactly with every aspect of the job description, you are encouraged to apply. You may be the right candidate for this or another opportunity, and the recruitment team will support you in exploring where your skills fit best.

## What's in it for you

• Opportunity to work on enterprise-scale AI, machine learning, and big data initiatives.
• Exposure to modern cloud-native platforms, MLOps technologies, and AI engineering practices.
• Collaboration with multidisciplinary teams across data science, software engineering, platform engineering, and business functions.
• Involvement in designing and operating scalable AI solutions that have a measurable business impact.
• Opportunity to contribute to innovation, automation, and continuous improvement initiatives within a global technology environment.

## What skills you will learn

• Advanced MLOps lifecycle management and production AI operations.
• Enterprise AI platform engineering and cloud-based machine learning services.
• Model monitoring, drift detection, observability, and operational excellence practices.
• CI/CD implementation for machine learning and AI workloads.
• Large-scale distributed data processing using Spark and Hadoop ecosystems.
• Containerisation and orchestration using Docker and Kubernetes.
• Responsible AI, governance, security, and compliance practices for enterprise AI solutions.
• Integration of LLM-powered capabilities into business applications and services.

## VOIS Equal Opportunity Employer Commitment

Vodafone recognises and celebrates the value of diversity in building a workforce that reflects the customers and communities it serves. No form of discrimination is tolerated. This includes, but is not limited to, discrimination based on race, colour, age, veteran status, gender identity, gender expression, sexual orientation, pregnancy, maternity or parental status, ethnicity, disability, religion or belief, political affiliation, trade union membership, nationality, citizenship, indigenous status, medical condition, HIV status, neurodiversity, social origin, cultural background, marital or civil partnership status, or socio-economic background.

## Join Us

At Vodafone, we’re working hard to build a better future. A more connected, inclusive and sustainable world. As a dynamic global community, it's our human spirit, together with technology, that empowers us to achieve this.

We challenge and innovate in order to connect people, businesses, and communities across the world. Delighting our customers and earning their loyalty drive us, and we experiment, learn fast and get it done, together.

With us, you can truly be yourself and belong, share inspiration, embrace new opportunities, thrive, and make a real difference.

## Alert

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#JDEnhancedByTARA

## Follow us on social media

• LinkedIn: VOIS LinkedIn
• Facebook: VOIS Facebook
• Instagram: VOIS Instagram
• You can also chat with our employees to learn more about our projects: Employee Connect