Machine Learning Engineer - #137055

About WorkBuzz

WorkBuzz is a well-funded SaaS company, with a vision to help improve the working lives of 1 million people.

The world of work has changed with five generations working together for the first time in history and the recent advances of AI. In addition, 80% of workers are now either remote, in the field, on the frontline or on the shop floor. Trusted by over 400 organisations, we help forward-thinking companies that have different types of employees in different locations navigate these challenges, build great cultures and make better informed people decisions – reaching all of their employees, wherever they are, whatever they do.

Generative AI and Large Language Models are making us rethink how we engage with our workforce.  The future isn’t 50 question surveys, it’s nuanced conversations powered by AI, that can happen simultaneously, globally and in different languages.  

We are harnessing AI in our listening and engagement platform, using it to analyse large swathes of data and extract insight and meaning, signposting users to what really matters and where they should drive change in their teams.   We are also pioneering a world-first feedback product that is geared around generations that have grown up consuming short form video and social media.  

Collectively, our vision is to help improve the working lives of 1 million people and we need the best talent – this is where you come in! 

Role Overview

We are looking for a Machine Learning Engineer to lead the deployment and scaling of ML models in production. This role is hands-on, requiring experience in building, optimising, and deploying ML models in AWS while ensuring reliability, performance, and security. 

You will have experience with data science and you will work closely with Backend Engineers, and DevOps teams to transform machine learning research into scalable, production-ready applications. The ideal candidate is experienced in MLOps, AWS Bedrock, SageMaker, data pipelines, and model lifecycle management.  You must be able to support the full lifecycle from taking code in notebook into production and know how to monitor it and ensure it’s accuracy, performance, reliability and security. 

This is an opportunity to have a huge impact on our AI capabilities, ensuring that machine learning models are robust, efficient, and fully integrated into our SaaS platform. 

Key Responsibilities

ML Model Deployment & Productionisation 

• Design and implement scalable, efficient, and secure ML model deployment pipelines in AWS. 
• Optimize models for performance, latency, and cost efficiency in production. 
• Automate model training, testing, and deployment workflows using CI/CD pipelines. 
• Develop robust monitoring, logging, and alerting systems to track model performance. 
  
  

MLOps & Infrastructure 

• Own the end-to-end ML lifecycle, from development to deployment and maintenance. 
• Implement ML model versioning, reproducibility, documentation and governance best practices. 
• Work with AWS Bedrock, SageMaker, Lambda, Fargate, and ECS to deploy ML models at scale. 
• Develop feature stores, data pipelines, and real-time inference architectures. 
• Improve model explainability, bias detection, and drift monitoring. 
  
  

Collaboration & Technical Leadership 

• Work closely with Data Scientists to turn research into scalable applications. 
• Partner with DevOps and Backend Engineers to ensure seamless integration with our SaaS platform. 
• Drive adoption of MLOps best practices across the team. 
• Provide technical mentorship and contribute to ML engineering standards.
  
  

Key Requirements

• 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar. 
• Experience deploying and managing ML models in production using AWS (Bedrock, SageMaker, Lambda, S3, SQS, DynamoDB, etc.). 
• Familiarity with NLP, LLMs, or AI-powered SaaS applications, such as the Claude Foundation Model from Anthropic. 
• Strong proficiency in Python, TensorFlow/PyTorch, and Scikit-learn. 
• Experience in data pipeline development using Airflow, Spark, or similar tools. 
• Deep understanding of CI/CD for ML (GitLab CI/CD, MLflow, Docker, ECS). 
• Experience in API development and integrating ML models with backend systems. 
• Strong knowledge of cloud security, IAM, and cost optimization in AWS.
  
  

Nice-to-Have: 

• Experience with real-time ML inference architectures (e.g., AWS Lambda, DynamoDB Streams). 
• Knowledge of vector databases and retrieval-augmented generation (RAG). 
• Exposure to ECS, Kubernetes, Terraform, and Infrastructure as Code (IaC). 
  
  

Why you’ll love working at WorkBuzz

• Our culture – WorkBuzz is fast-paced, dynamic and rewarding. Everyone has a voice and the smartest idea wins. 
• Clear purpose – be part of building something which matters. 
• Be the best you can be – we will invest in your development and help you grow. 
• Competitive salary, share options, private health insurance and a range of other benefits 
• Flexible/ hybrid working – the opportunity to split your time between home working and our Milton Keynes office. 
• Check out our Glassdoor reviews!