Senior Machine Learning Engineer

Sportyjob; London; On-site; GBP 60,000 - 100,000; Full time

Job summary

An innovative company is seeking a Senior Machine Learning Engineer to lead MLOps initiatives and enhance AI solutions. In this role, you'll be pivotal in the lifecycle of machine learning models, from training to deployment, ensuring their seamless integration into production. You will design and build the necessary infrastructure, implement CI/CD pipelines, and develop monitoring frameworks to optimize model performance. If you are passionate about machine learning and want to work in a collaborative environment, this opportunity is perfect for you.

Qualifications

• 5+ years of experience in Machine Learning with MLOps focus.
• Proficient in cloud platforms and infrastructure-as-code tools.

Responsibilities

• Lead MLOps strategy implementation and model monitoring.
• Design infrastructure for scalable model deployment.

Skills

MLOps

Machine Learning

CI/CD Pipelines

Cloud Platforms

Containerization (Docker)

Orchestration (Kubernetes)

Real-time Data Streaming (Kafka)

Monitoring Systems

Communication Skills

Education

Bachelor's Degree in Computer Science or related field

Tools

Terraform

Google Cloud Platform

Docker

Kubernetes

LangChain

LangFuse

Apache Airflow

Job description

Team for Career Site

Technology

In short

As a Senior Machine Learning Engineer at On, you’ll play a critical role in the full lifecycle of our machine learning models. Besides being responsible for training and deploying models, you will spearhead our MLOps initiatives to ensure their seamless and efficient integration and operation in production. This includes championing MLOps best practices, enhancing deployment processes, developing essential tooling and automation to maximize the impact of our AI solutions, and implementing robust monitoring to optimize performance and reliability.

Your mission

1. Lead the implementation and continuous improvement of our MLOps strategy, establishing best practices for model development, deployment, and monitoring.
2. Create and train machine learning models to solve specific business problems, such as product recommendations, customer segmentation, and demand forecasting. Implement such models into production systems to make predictions, drive real-time personalization, and support decision-making.
3. Design and build the necessary infrastructure and tooling to support efficient and scalable model deployment, including CI/CD pipelines and automated testing.
4. Implement and own Terraform to manage and provision our cloud infrastructure for machine learning operations.
5. Oversee the transition to a real-time streaming architecture for our machine learning applications, ensuring efficient data ingestion, feature engineering, and model serving in a streaming context.
6. Develop and implement a comprehensive monitoring framework to track model performance, identify potential issues, and ensure optimal model health in production. Monitor model performance and update models as needed to adapt to new data and changing conditions.
7. Collaborate closely with data scientists and engineers to ensure seamless integration of models into our existing systems and workflows. Stay abreast of the latest MLOps trends and technologies to continuously improve our processes and tools.

Your story

1. You have 5+ years of experience as a Machine Learning Engineer with a strong focus on MLOps. You have a proven track record of successfully deploying and managing machine learning models in production environments.
2. You possess deep knowledge of MLOps principles, tools, and best practices.
3. You are proficient in cloud platforms (Google Cloud Platform is preferred), infrastructure-as-code tools like Terraform.
4. You have experience with CI/CD pipelines, containerization technologies (e.g., Docker), and orchestration tools (e.g., Kubernetes) and using orchestration tools such as Kubeflow (our preferred tool) or similar frameworks like Apache Airflow to manage and automate ML workflows.
5. You have experience with real-time data streaming technologies such as Kafka and Confluent and feature stores in such settings.
6. You are skilled in building and maintaining monitoring systems for machine learning models.
7. You have excellent communication and collaboration skills, enabling you to effectively work with cross-functional teams.

Bonus:

• Knowledge of frameworks such as LangChain used to orchestrate LLMs
• Experience in LLM evaluations, debugging, and monitoring using tools such as LangFuse or LangSmith

Meet the team

We’re a growing team of passionate Data Scientists and Machine Learning Engineers working across On to build creative and impactful models end-to-end that personalise experiences, optimise decision-making, and predict future trends. We sit within Technology and have the opportunity to collaborate across On – Optimising how we use data, how we consume data, and how we support On’s growth through data is something you could be a part of. We’d love to hear from you!