Machine Learning Engineer

Location: Remote

About Ovyo

Ovyo is a B2B services company providing flexible engineering teams and talent solutions to the streaming, media, satellite, and communications industries. With a global footprint across the UK, India, Portugal, South Africa, Brazil, the US, and Eastern Europe, we work on a mix of long-term client engagements and fast-paced consulting projects. Our engineers build the platforms that shape how the world consumes video and connects — while accelerating their own careers along the way.

The Role

We’re looking for a Machine Learning Engineer to design, build, and operate large-scale machine learning systems, with a strong focus on recommendation engines and personalised content discovery.

You’ll work closely with client engineering and product teams to deliver production-ready ML solutions that are scalable, reliable, and high-performance in real-world environments.

Responsibilities

- Design, build, deploy, and optimise end-to-end machine learning solutions in production

- Develop and enhance recommendation systems supporting personalised user experiences

- Integrate ML models with data pipelines and backend platforms

- Collaborate with engineers, product managers, and stakeholders to deliver ML-driven features

- Ensure ML systems are scalable, observable, and reliable

- Communicate technical concepts and insights clearly to both technical and non-technical audiences

Requirements

- Proven experience as a Machine Learning Engineer working with large-scale data systems

- Strong hands-on experience with Java, Python, and SQL

- Experience building and deploying production-grade machine learning models

- Hands-on experience with TensorFlow 2.x

- Solid understanding of recommendation systems, including Matrix Factorization and Factorization Machines

- Experience with Apache Spark and Apache Flink

- Experience working in Kubernetes (K8s) environments

- Familiarity with OpenSearch or similar search technologies

- Hours flexibility - some cross over with the US teams

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